Merging mechanisms, as a mature business model in the field of online advertising, refers to the practice where platforms sort and display sponsored ads provided by advertisers alongside organic results to users according to specific rules. However, in real-world industrial scenarios, advertisers are gradually adopting autobidding instead of manual bidding—they only need to provide high-level constraints like target Return-on-Spend (tROS) to the agent, which then bids on their behalf to maximize multi-round value. Existing studies often overlook this actual business form, resulting in suboptimal outcomes. Meanwhile, the coexistence of the same item in both ad and organic result forms within merging mechanisms further increases the complexity of the context. In terms of interests, advertisers aim to maximize conversion value, while platforms seek to increase the revenue while ensuring user experience, thereby enhancing reputation. Nevertheless, existing works often fail to address this multi-stakeholder challenge in the modern auto-bidding era. To address these issues, we introduce IMPACTNet, an end-to-end framework based on automated mechanism design that learns a unified allocation and pricing mechanism. IMPACTNet directly incorporates advertisers' tROS constraints, models complex contextual information using a transformer-based architecture, and introduces a learnable, state-aware de-duplication strategy. By formulating the design as a constrained optimization problem, our framework learns a mechanism that ensures Auto-bidding Incentive Compatibility (AIC), ensuring truthfully reporting tROS a dominant strategy. Extensive experiments on synthetic and large-scale industrial datasets demonstrate that IMPACTNet significantly outperforms established baselines, achieving a better balance of platform objectives, user experience, and advertiser tROS satisfaction.

Time-series forecasting in real-world applications such as finance and energy often faces challenges due to limited training data and complex, noisy temporal dynamics. Existing deep forecasting models typically supervise predictions using full-length temporal windows, which include substantial high-frequency noise and obscure long-term trends. Moreover, auxiliary variables containing rich domain-specific information are often underutilized, especially in few-shot settings. To address these challenges, we propose LoFT-LLM, a frequency-aware forecasting pipeline that integrates low-frequency learning with semantic calibration via a large language model (LLM). Firstly, a Patch Low-Frequency forecasting Module (PLFM) extracts stable low-frequency trends from localized spectral patches. Secondly, a residual learner then models high-frequency variations. Finally, a fine-tuned LLM refines the predictions by incorporating auxiliary context and domain knowledge through structured natural language prompts. Extensive experiments on financial and energy datasets demonstrate that LoFT-LLM significantly outperforms strong baselines under both full-data and few-shot regimes, delivering superior accuracy, robustness, and interpretability.

Query-to-bidword (i.e., bidding keyword) rewriting is fundamental to sponsored search, transforming noisy user queries into semantically relevant and commercially valuable keywords. Recent advances in large language models (LLMs) improve semantic relevance through generative retrieval frameworks, but they rarely encode the commercial value of keywords. As a result, rewrites are often semantically correct yet economically suboptimal, and a reinforcement learning from human feedback (RLHF) stage is usually added after supervised fine-tuning (SFT) to mitigate this deficiency. However, conventional preference alignment frequently overemphasize the ordering of bidword values and is susceptible to overfitting, which degrades rewrite quality. In addition, bidword value changes rapidly, while existing generative methods do not respond to these fluctuations. To address this shortcoming, we introduce VALUE (Value-Aware Large language model for qUery rewriting via wEighted trie), a framework that integrates value awareness directly into generation and enhances value alignment during training. VALUE employs the Weighted Trie, a novel variant of the classical trie that stores real-time value signals for each token. During decoding, the framework adjusts the LLM's token probabilities with these signals, constraining the search space and steering generation toward high-value rewrites. The alignment stage uses a fine-grained preference learning strategy that emphasizes stable, high-value differences and down-weights noisy or transient fluctuations, thereby improving robustness and reducing overfitting. Offline experiments show that VALUE significantly outperforms baselines in both semantic matching and value-centric metrics. Online A/B tests further revealed that our Revenue Per Mille (RPM) metric increased by 1.64%. VALUE has been deployed on our advertising system since October 2024 and served the Double Eleven promotions, the biggest shopping carnival in China.

Auto-bidding and budget pacing are formulated as sequential decision-making tasks. While flexible, such a framework faces a fundamental granularity mismatch: decisions are made at a fine temporal scale, while their performance feedback is fully and reliably observable at a much coarser resolution. This manifests as sparse reward signals and delayed feedback, forcing agents to learn from locally noisy and incomplete signals. We address this core challenge by introducing the Trajectory Auto-Regressive Model (TAR), a generative framework that aligns planning resolution with feedback dynamics. Motivated by the insight that coarser temporal aggregation yields denser rewards and less scattered feedback, TAR generates trajectories in a coarse-to-fine manner. It incorporates three key innovations: (1) progressive trajectory generation across multiple temporal scales; (2) latent-space compression via a multi-scale VQVAE to handle heterogeneous feature types; and (3) state-action integration that captures long-term dependencies without auxiliary inverse models. Comprehensive experiments in both sparse-reward and delayed-feedback settings demonstrate that TAR consistently outperforms strong baselines in offline simulations and online deployment, validating its effectiveness in overcoming the granularity mismatch for more stable and robust advertising optimization.

Generative recommendation (GR) has shown great promise in industrial applications, particularly for candidate generation and end-to-end recommendations. However, existing GR training paradigms suffer from two fundamental mismatches with real-world deployment requirements. First, they optimize for point-wise prediction of a single ground-truth item, whereas practical systems must produce a diverse, high-value set of candidates. Second, they treat all user interactions as equally informative, ignoring their inherent differences in utility. Although reward-based fine-tuning offers a partial remedy, it often lacks token-level supervision. To address these challenges, we reformulate GR as a sequential set-generation problem and propose GFlowGR, a GFlowNet-based fine-tuning framework that explicitly aligns generation probabilities with item-level utilities. GFlowGR comprises three tightly integrated components, each addressing a key limitation of conventional fine-tuning: a trajectory sampler that constructs training trajectories from candidate sets to enable set-wise learning, a behavior-aware reward model that quantifies item utility to support value-aware optimization, and a GFlowNet objective that provides token-level supervision. Extensive experiments on three real-world datasets with two representative LLM-based GR backbones show consistent and significant improvements over strong baselines, validating the effectiveness of our approach. For real-world deployment, GFlowGR has been integrated into Taobao 's search advertising businesses, delivering a 0.4% relative improvement in annual revenue since its launch in mid-2025, corresponding to billion-level monetary gains. Code is available at https://github.com/Applied-Machine-Learning-Lab/SIGIR26_GFlowGR.

Recent advancements in autoregressive Large Language Models (LLMs) have achieved remarkable progress, largely driven by their scalability—commonly formalized as the scaling law. Inspired by these successes, there has been growing interest in adapting LLMs to recommendation systems (RecSys) by reformulating recommendation tasks as generative sequence modeling problems. However, existing End-to-End Generative Recommendation (E2E-GR) methods often sacrifice the practical advantages of traditional Deep Learning-based Recommendation Models (DLRMs)—including mature feature engineering, modular architectures, and production-grade optimization practices. This trade-off introduces critical challenges that hinder the effective application of scaling laws in industrial RecSys. In this paper, we present Large User Model (LUM), a scalable and production-aware framework that bridges the gap between generative modeling and industrial recommendation requirements. LUM addresses these limitations through a principled three-step paradigm, designed to preserve the flexibility of autoregressive generation while maintaining compatibility with real-world deployment constraints. Extensive experiments show that LUM outperforms state-of-the-art DLRMs and E2E-GR approaches across multiple benchmarks. Notably, LUM exhibits strong scalability: performance improves consistently as the model scales up to 7 billion parameters. Furthermore, LUM has been successfully deployed in a large-scale industrial application, where it delivered statistically significant gains in a live A/B test, demonstrating both its effectiveness and practical viability.

With the rapid advancement of e-commerce, exploring general representations rather than task-specific ones has attracted increasing research attention. For product understanding, although existing discriminative dual-flow architectures drive progress in this field, they inherently struggle to model the many-to-one alignment between multiple images and texts of products. Therefore, we argue that generative Multimodal Large Language Models (MLLMs) hold significant potential for improving product representation learning. Nevertheless, achieving this goal still remains non-trivial due to several key challenges: the lack of multimodal and aspect-aware modeling modules in typical LLMs; the common presence of background noise in product images; and the absence of a standard benchmark for evaluation. To address these issues, we propose the first generative MLLM-based model named MOON for product representation learning. Our method (1) employs a guided Mixture-of-Experts (MoE) module for targeted modeling of multimodal and aspect-specific product content; (2) effectively detects core semantic regions in product images to mitigate the distraction and interference caused by background noise; and (3) introduces the specialized negative sampling strategy to increase the difficulty and diversity of negative samples. In addition, we release a large-scale multimodal benchmark MBE for various product understanding tasks. Experimentally, our model demonstrates competitive zero-shot performance on both our benchmark and the public dataset, showcasing strong generalization across various downstream tasks, including cross-modal retrieval, product classification, and attribute prediction. Furthermore, the case study and visualization illustrate the effectiveness of MOON for product understanding.

Auto-bidding plays an essential role in online advertising, allowing agents to automatically adjust bids for advertisers. Recently, the rise of Marketing Management service in e-commerce platforms has driven the evolution from auto-bidding to auto-marketing, enabling merchants to delegate their advertising bidding and product's coupon discounting decisions to agents. Auto-marketing requires agents to jointly decide on bidding and coupon discounting. Furthermore, compared to classic static constraints, auto-marketing agent faces a self-funding constraint (where the budget for both bidding and coupon discounting is entirely derived from the agent's commission revenue). Existing rule-based or RL-based methods often struggle with dynamic environments and complex sequential dependencies. To overcome these limitations, we propose a Generative Auto-Marketing framework (GAM), designed for performing joint sequential decisions on bidding and coupon discounting, and optimizing business objectives through post-training alignment. Furthermore, GAM employs a flexible, constraint-aware reward alignment module, and utilizes Group Relative Policy Optimization (GRPO) to align the pre-trained model, thus empirically balancing objective maximization and constraint satisfaction. We construct an offline simulation environment based on large-scale real-world dataset, and demonstrate the effectiveness of GAM through extensive experimental results.

Identifying high-revenue mechanisms that are both dominant strategy incentive compatible (DSIC) and individually rational (IR) is a fundamental challenge in auction design. While theoretical approaches have encountered bottlenecks in multi-item combinatorial auctions, there has been much empirical progress in the automated design of such mechanisms using machine learning. However, existing research primarily focuses on randomized auctions, with less attention given to more practical deterministic auctions. Therefore, in this paper, we introduce OD-VVCA, an objective decomposition approach for automated designing revenue-maximizing deterministic Virtual Valuations Combinatorial Auctions (VVCAs), which are inherently DSIC and IR. We use a parallelizable dynamic programming algorithm to compute the allocation and revenue outcomes of a VVCA efficiently. We then decompose the revenue objective function into continuous and piecewise-constant discontinuous components, optimizing each using distinct methods. Extensive experiments show that OD-VVCA achieves high revenue in multi-item auctions, especially in large-scale settings where it outperforms both randomized and deterministic baselines, indicating its efficacy and scalability.

The prediction objectives of online advertisement ranking models are evolving from probabilistic metrics like conversion rate (CVR) to numerical business metrics like post-click gross merchandise volume (GMV). Unlike the well-studied delayed feedback problem in CVR prediction, delayed feedback modeling for GMV prediction remains unexplored and poses greater challenges, as GMV is a continuous target, and a single click can lead to multiple purchases that cumulatively form the label.

In industrial recommender systems, conversion rate (CVR) is often used for traffic allocation, but fails to fully reflect recommendation effectiveness as it does not account for refund rate (RFR). Thus, net conversion rate (NetCVR), the probability that a clicked item is purchased and not refunded, is proposed to better show true user satisfaction and business value. Unlike CVR, NetCVR prediction involves a more complex multi-stage cascaded delay feedback phenomenon. The two cascaded delays Click->Conversion and Conversion->Refund in NetCVR have opposite effects. Therefore, traditional CVR methods cannot be directly applied. At present, the lack of relevant open-source datasets and online continuous training schemes poses a challenge. To address these, we first introduce CAscadal Sequences of Conversion And Delayed rEfund (CASCADE), the first large-scale open dataset derived from Taobao app for online continuous NetCVR prediction. We further analyze CASCADE and derive three key insights: (1) NetCVR exhibits clear temporal patterns necessitating online continuous modeling; (2) Cascaded modeling CVR and RFR for NetCVR outperforms directly modeling NetCVR; and (3) delay time, which correlated with both CVR and RFR, is an important feature for NetCVR prediction. Based on these insights, we propose neT convErsion caScaded modeLing and debiAsing method (TESLA). This continuous method features a CVR-RFR cascaded architecture, stage-wise debiasing, and a delay-time-aware ranking loss for efficient NetCVR prediction. Experiments show that TESLA outperforms state-of-the-art methods on CASCADE, achieving an absolute improvement of 12.41% in RI-AUC and 14.94% in RI-PRAUC on NetCVR over the strongest baseline. We hope this work provides a new direction for online delayed feedback modeling in NetCVR prediction. Our code and dataset are available at https://github.com/alimama-tech/NetCVR.

Generative Recommendation (GR), powered by Large Language Models (LLMs), represents a promising new paradigm for industrial recommender systems. However, their practical application is severely hindered by high inference latency, making them infeasible for high-throughput, real-time services and limiting their overall business impact. While Speculative Decoding (SD) has been proposed to accelerate the autoregressive generation process, existing implementations introduce new bottlenecks: they typically require separate draft models and model-based verifiers, which require additional training and increase latency overhead. In this paper, we address these challenges with NEZHA, a novel architecture that achieves hyperspeed decoding for GR systems without sacrificing recommendation quality. Specifically, NEZHA integrates a nimble autoregressive draft head directly into the primary model, enabling efficient self-drafting. This design, combined with a specialized input prompt structure, preserves the integrity of sequence-to-sequence generation. Furthermore, to tackle the critical problem of hallucination—a major source of performance degradation—we introduce an efficient, model-free verifier based on a hash set. We demonstrate the effectiveness of NEZHA through extensive experiments on public datasets and have successfully deployed the system on Taobao since October 2025, achieving 1.2% business improvement, translating to billion-level advertising revenue and serving hundreds of millions of daily active users. The code is available at https://github.com/Applied-Machine-Learning- Lab/WWW2026_NEZHA.

Conversion rate (CVR) prediction is a core component of online advertising systems, where the attribution mechanisms-rules for allocating conversion credit across user touchpoints-fundamentally determine label generation and model optimization. While many industrial platforms support diverse attribution mechanisms (e.g., First-Click, Last-Click, Linear, and Data-Driven Multi-Touch Attribution), conventional approaches restrict model training to labels from a single production-critical attribution mechanism, discarding complementary signals in alternative attribution perspectives.

On e-commerce platforms, sellers typically bid for impressions from ad traffic to promote their products. However, for most sellers, the majority of their sales come from organic traffic. Consequently, the relationship between their ad spending and total sales remains uncertain, resulting in operational inefficiency. To address this issue, e-commerce platforms have recently introduced a novel platform-wide marketing service known as QuanZhanTui, which has reportedly enhanced marketing efficiency for sellers and driven substantial revenue growth for platforms. QuanZhanTui allows sellers to bid for impressions from the platform's entire traffic to boost their total sales without compromising the platform's user experience. In this paper, we investigate the mechanism design problem that arises from QuanZhanTui. The problem is formulated as a multi-objective optimization to balance sellers' welfare and platform's user experience. We first introduce the stock-constrained value maximizer model, which reflects sellers' dual requirements on marketing efficiency and platform-wide ROI. Then, we propose the Liquid Payment Auction (LPA), an auction designed to optimize the balanced objectives while accounting for sellers' requirements in the auto-bidding environment. It employs a simple payment rule based on sellers' liquid welfare, providing a clearer link between their investment and total sales. Under mild assumptions, we theoretically prove desirable properties of LPA, such as optimality and incentive compatibility. Extensive experiments demonstrate LPA's superior performance over conventional auctions in QuanZhanTui.

In online advertising, advertisers commonly utilize auto-bidding services to bid for impression opportunities. A typical objective of the auto-bidder is to optimize the advertiser's cumulative value of winning impressions within specified budget constraints. However, such a problem is challenging due to the complex bidding environment faced by diverse advertisers. To address this challenge, we introduce ABPlanner, a few-shot adaptable budget planner designed to improve budget-constrained auto-bidding. ABPlanner is based on a hierarchical bidding framework that decomposes the bidding process into shorter, manageable stages. Within this framework, ABPlanner allocates the budget across all stages, allowing a low-level auto-bidder to bids based on the budget allocation plan. The adaptability of ABPlanner is achieved through a sequential decision-making approach, inspired by in-context reinforcement learning. For each advertiser, ABPlanner adjusts the budget allocation plan episode by episode, using data from previous episodes as prompt for current decisions. This enables ABPlanner to quickly adapt to different advertisers with few-shot data, providing a sample-efficient solution. Extensive simulation experiments and real-world A/B testing validate the effectiveness of ABPlanner, demonstrating its capability to enhance the cumulative value achieved by auto-bidders.

Auto-bidding is crucial in facilitating online advertising by automatically providing bids for advertisers. While previous work has made great efforts to model bidding environments for better ad performance, it has limitations in generalizability across environments since these models are typically tailored for specific bidding scenarios. To this end, we approach the scenario-independent principles through a unified function that estimates the achieved effect under specific bids, such as budget consumption, gross merchandise volume (GMV), page views, etc. Then, we propose a bidding foundation model Bid2X to learn this fundamental function from data in various scenarios. Our Bid2X is built over uniform series embeddings that encode heterogeneous data through tailored embedding methods. To capture complex inter-variable and dynamic temporal dependencies in bidding data, we propose two attention mechanisms separately treating embeddings of different variables and embeddings at different times as attention tokens for representation learning. On top of the learned variable and temporal representations, a variable-aware fusion module is used to perform adaptive bidding outcome prediction. To model the unique bidding data distribution, we devise a zero-inflated projection module to incorporate the estimated non-zero probability into its value prediction, which makes up a joint optimization objective containing classification and regression. The objective is proven to converge to the zero-inflated distribution.

In the field of auction design, leveraging deep learning to solve optimal auctions from sampled data has become a promising direction. However, real-world contexts often involve uncertain data, which would severely affect the auction performance, but it is lacking consideration in existing works. To address this challenge, we incorporate these uncertainties into auction design metrics, and frame this challenge as a robust data-driven auction design problem. To solve this problem, we first propose the GAT method, where we introduce the process of problem relaxation and transformation to address the non-differentiable variable presented in the original problem, and further propose an adversarial training algorithm to solve the mini-max problem after transformation. Moreover, to obtain moderately robust auctions, we propose two methods to select the robust coefficient, which provides guidance and insights for selecting robust auctions based on generalization and performance metrics. Finally, with the insights from the GAT method, we further propose the SAT method, where we employ a strict and unified IC constraint that extends from the GAT method, which provides strong IC guarantees and stable revenue in uncertain environments. Experiments on both constructed and real-world datasets show that our robust methods effectively improve the performance of auctions in terms of revenue and IC guarantees.

Large language models (LLMs) achieve remarkable success in natural language processing (NLP). In practical scenarios like recommendations, as users increasingly seek personalized experiences, it becomes crucial to incorporate user interaction history into the context of LLMs to enhance personalization. However, from a practical utility perspective, user interactions' extensive length and noise present challenges when used directly as text prompts. A promising solution is to compress and distill interactions into compact embeddings, serving as soft prompts to assist LLMs in generating personalized responses. Although this approach brings efficiency, a critical concern emerges: Can user embeddings adequately capture valuable information and prompt LLMs? To address this concern, we propose UQABench, a benchmark designed to evaluate the effectiveness of user embeddings in prompting LLMs for personalization. We establish a fair and standardized evaluation process, encompassing pre-training, fine-tuning, and evaluation stages. To thoroughly evaluate user embeddings, we design three dimensions of tasks: sequence understanding, action prediction, and interest perception. These evaluation tasks cover the industry's demands in traditional recommendation tasks, such as improving prediction accuracy, and its aspirations for LLM-based methods, such as accurately understanding user interests and enhancing the user experience. We conduct extensive experiments on various state-of-the-art methods for modeling user embeddings. Additionally, we reveal the scaling laws of leveraging user embeddings to prompt LLMs. The benchmark is available online at https://github.com/OpenStellarTeam/UQABench.

Online advertising has become a core revenue driver for internet industry, with ad auctions playing a crucial role in ensuring platform revenue and advertiser incentives. Classical auction mechanisms, such as GSP, rely on the independent CTR assumption and fail to account for the interplay among the displayed items, also called as externalities in economics. Recent advancements in learning-based auctions enable the encoding of high-dimensional contextual features. However, existing methods are limited by the ''prediction-before-allocation'' design paradigm, which models set-level externalities within candidate ads and fails to consider the context of the final allocation, leading to suboptimal results. In this work, we introduce Contextual Generative Auction (CGA), a novel framework that incorporates permutation-level externalities in multi-slot ad auctions. Built on the structure of our theoretically derived optimal auction, CGA decouples the optimization of allocation and payment. We construct an autoregressive generative model for allocation, and reformulate incentive compatibility (IC) constraint into minimizing ex-post regret that supports gradient computation, enabling end-to-end learning of the optimal payment rule. Extensive offline and online experiments demonstrate that CGA significantly enhances platform revenue and CTR compared to existing methods, and effectively approximates the optimal auction with nearly maximal revenue and minimal regret.

Although multi-task learning (MTL) has been a preferred approach and successfully applied in many real-world scenarios, MTL models are not guaranteed to outperform single-task models on all tasks mainly due to the negative effects of conflicting gradients among the tasks. In this paper, we fully examine the influence of conflicting gradients and further emphasize the importance and advantages of achieving non-conflicting gradients which allows simple but effective trade-off strategies among the tasks with stable performance. Based on our findings, we propose the Gradient Deconfliction via Orthogonal Projections onto Subspaces (GradOPS) spanned by other task-specific gradients. Our method not only solves all conflicts among the tasks, but can also effectively search for diverse solutions towards different trade-off preferences among the tasks. Theoretical analysis on convergence is provided, and performance of our algorithm is fully testified on multiple benchmarks in various domains. Results demonstrate that our method can effectively find multiple state-of-the-art solutions with different trade-off strategies among the tasks on multiple datasets.

The standard framework of online bidding algorithm design assumes that the seller commits himself to faithfully implementing the rules of the adopted auction.However, the seller may attempt to cheat in execution to increase his revenue if the auction belongs to the class of non-credible auctions.For example, in a second-price auction, the seller could create a fake bid between the highest bid and the second highest bid.This paper focuses on one such case of online bidding in repeated second-price auctions.At each time 𝑡, the winner with bid 𝑏 𝑡 is charged not the highest competing bid 𝑑 𝑡 but a manipulated price 𝑝 𝑡 = 𝛼 0 𝑑 𝑡 + (1 -𝛼 0 )𝑏 𝑡 , where the parameter 𝛼 0 ∈ [0, 1] in essence measures the seller's credibility.Unlike classic repeated-auction settings where the bidder has access to samples (𝑑 𝑠 ) 𝑡 -1 𝑠=1 , she can only receive mixed signals of𝑠=1 and 𝛼 0 in this problem.The task for the bidder is to learn not only the bid distributions of her competitors but also the seller's credibility.We establish regret lower bounds in various information models and provide corresponding online bidding algorithms that can achieve near-optimal performance.Specifically, * Both authors contributed equally to this research.

Despite the recognized potential of multimodal data to improve model accuracy, many large-scale industrial recommendation systems, including Taobao display advertising system, predominantly depend on sparse ID features in their models. In this work, we explore approaches to leverage multimodal data to enhance the recommendation accuracy. We start from identifying the key challenges in adopting multimodal data in a manner that is both effective and cost-efficient for industrial systems. To address these challenges, we introduce a two-phase framework, including: 1) the pre-training of multimodal representations to capture semantic similarity, and 2) the integration of these representations with existing ID-based models. Furthermore, we detail the architecture of our production system, which is designed to facilitate the deployment of multimodal representations. Since the integration of multimodal representations in mid-2023, we have observed significant performance improvements in Taobao display advertising system. We believe that the insights we have gathered will serve as a valuable resource for practitioners seeking to leverage multimodal data in their systems.

Online advertising platforms leverage a two-stage auction architecture to deliver personalized ads to users with low latency. The first stage efficiently selects a small subset of promising candidates out of the complete pool of ads. In the second stage, an auction is conducted within the subset to determine the winning ad for display, using click-through-rate predictions from the second-stage machine learning model. In this work, we investigate the online learning process of the first-stage subset selection policy, while ensuring game-theoretic properties in repeated two-stage ad auctions. Specifically, we model the problem as designing a combinatorial bandit mechanism with a general reward function, as well as additional requirements of truthfulness and individual rationality (IR). We establish an O(T) regret lower bound for truthful bandit mechanisms, which demonstrates the challenge of simultaneously achieving allocation efficiency and truthfulness. To circumvent this impossibility result, we introduce truthful α-approximation oracles and evaluate the bandit mechanism through α-approximation regret. Two mechanisms are proposed, both of which are ex-post truthful and ex-post IR. The first mechanism is an explore-then-commit mechanism with regret O(T2/3 ), and the second mechanism achieves an improved O(log T /ΔΦ2) regret where ΔΦ is a distribution-dependent gap, but requires additional assumptions on the oracles and information about the strategic bidders.

Auto-bidding plays a crucial role in facilitating online advertising by automatically providing bids for advertisers. Reinforcement learning (RL) has gained popularity for auto-bidding. However, most current RL auto-bidding methods are modeled through the Markovian Decision Process (MDP), which assumes the Markovian state transition. This assumption restricts the ability to perform in long horizon scenarios and makes the model unstable when dealing with highly random online advertising environments. To tackle this issue, this paper introduces AI-Generated Bidding (AIGB), a novel paradigm for auto-bidding through generative modeling. In this paradigm, we propose DiffBid, a conditional diffusion modeling approach for bid generation. DiffBid directly models the correlation between the return and the entire trajectory, effectively avoiding error propagation across time steps in long horizons. Additionally, DiffBid offers a versatile approach for generating trajectories that maximize given targets while adhering to specific constraints. Extensive experiments conducted on the real-world dataset and online A/B test on Alibaba advertising platform demonstrate the effectiveness of DiffBid, achieving 2.81% increase in GMV and 3.36% increase in ROI.

In machine learning systems, privileged features refer to the features that are available during offline training but inaccessible for online serving. Previous studies have recognized the importance of privileged features and explored ways to tackle online-offline discrepancies. A typical practice is privileged features distillation (PFD): train a teacher model using all features (including privileged ones) and then distill the knowledge from the teacher model using a student model (excluding the privileged features), which is then employed for online serving. In practice, the pointwise cross-entropy loss is often adopted for PFD. However, this loss is insufficient to distill the ranking ability for CTR prediction. First, it does not consider the non-i.i.d. characteristic of the data distribution, i.e., other items on the same page significantly impact the click probability of the candidate item. Second, it fails to consider the relative item order ranked by the teacher model's predictions, which is essential to distill the ranking ability. To address these issues, we first extend the pointwise-based PFD to the listwise-based PFD. We then define the calibration-compatible property of distillation loss and show that commonly used listwise losses do not satisfy this property when employed as distillation loss, thus compromising the model's calibration ability, which is another important measure for CTR prediction. To tackle this dilemma, we propose Calibration-compatible LIstwise Distillation (CLID), which employs carefully-designed listwise distillation loss to achieve better ranking ability than the pointwise-based PFD while preserving the model's calibration ability. We theoretically prove it is calibration-compatible. Extensive experiments on public datasets and a production dataset collected from the display advertising system of Alibaba further demonstrate the effectiveness of CLID.

In the world of E-Commerce, the core task is to understand the personalized preference from various kinds of heterogeneous information, such as textual reviews, item images and historical behaviors. In current systems, these heterogeneous information are mainly exploited to generate better item or user representations. For example, in scenario of visual search, the importance of modeling query image has been widely acknowledged. But, these existing solutions focus on improving the representation quality of the query image, overlooking the personalized visual preference of the user. Note that the visual features affect the user's decision significantly, e.g., the user could be more likely to click the items with her preferred design. Hence, it is fruitful to exploit the visual preference to deliver better capacity for personalization.

In online advertising, advertisers participate in ad auctions to acquire ad opportunities, often by utilizing auto-bidding tools provided by demand-side platforms (DSPs). The current auto-bidding algorithms typically employ reinforcement learning (RL). However, due to safety concerns, most RL-based auto-bidding policies are trained in simulation, leading to a performance degradation when deployed in online environments. To narrow this gap, we can deploy multiple auto-bidding agents in parallel to collect a large interaction dataset. Offline RL algorithms can then be utilized to train a new policy. The trained policy can subsequently be deployed for further data collection, resulting in an iterative training framework, which we refer to as iterative offline RL. In this work, we identify the performance bottleneck of this iterative offline RL framework, which originates from the ineffective exploration and exploitation caused by the inherent conservatism of offline RL algorithms. To overcome this bottleneck, we propose Trajectory-wise Exploration and Exploitation (TEE), which introduces a novel data collecting and data utilization method for iterative offline RL from a trajectory perspective. Furthermore, to ensure the safety of online exploration while preserving the dataset quality for TEE, we propose Safe Exploration by Adaptive Action Selection (SEAS). Both offline experiments and real-world experiments on Alibaba display advertising platform demonstrate the effectiveness of our proposed method.

On typical e-commerce platforms, a product can be displayed to users in two possible forms, as an ad item or an organic item. Usually, ad and organic items are separately selected by the advertising system and recommendation system, and then combined by a content merging mechanism. Although the design of the content merging mechanism has been extensively studied, little attention has been given to a crucial situation where there is an overlap between candidate ad and organic items. Despite its common occurrence, this situation is not correctly handled by almost all existing works, potentially leading to incentive problems for advertisers and the violation of economic constraints. To address these issues, we revisit the design of the content merging mechanism. We introduce a necessary property called form stability, and provide simplification results of the mechanism design problem. Furthermore, we design two simple mechanisms strictly ensuring desired economic properties including incentive compatibility, and demonstrate their guaranteed performance through competitive ratio analysis under certain conditions.

Mobile Taobao Application delivers search services on multiple scenarios that take textual, visual, or product queries. This paper aims to propose a unified graph neural network for these search scenarios to leverage data from multiple scenarios and jointly optimize search performances with less training and maintenance costs. Towards this end, this paper proposes BOMGraph, BOosting Multi-scenario E-commerce Search with a unified Graph neural network. BOMGraph is embodied with several components to address challenges in multi-scenario search. It captures heterogeneous information flow across scenarios by inter-scenario and intra-scenario metapaths. It learns robust item representations by disentangling specific characteristics for different scenarios and encoding common knowledge across scenarios. It alleviates label scarcity and long-tail problems in scenarios with low traffic by contrastive learning with cross-scenario augmentation. BOMGraph has been deployed in production by Alibaba's E-commerce search advertising platform. Both offline evaluations and online A/B tests demonstrate the effectiveness of BOMGraph.

Click-Through Rate (CTR) prediction serves as a fundamental component in online advertising. A common practice is to train a CTR model on advertisement (ad) impressions with user feedback. Since ad impressions are purposely selected by the model itself, their distribution differs from the inference distribution and thus exhibits sample selection bias (SSB) that affects model performance. Existing studies on SSB mainly employ sample re-weighting techniques which suffer from high variance and poor model calibration. Another line of work relies on costly uniform data that is inadequate to train industrial models. Thus mitigating SSB in industrial models with a uniform-data-free framework is worth exploring. Fortunately, many platforms display mixed results of organic items (i.e., recommendations) and sponsored items (i.e., ads) to users, where impressions of ads and recommendations are selected by different systems but share the same user decision rationales. Based on the above characteristics, we propose to leverage recommendations samples as a free lunch to mitigate SSB for ads CTR model (Rec4Ad). After elaborating data augmentation, Rec4Ad learns disentangled representations with alignment and decorrelation modules for enhancement. When deployed in Taobao display advertising system, Rec4Ad achieves substantial gains in key business metrics, with a lift of up to +6.6% CTR and +2.9% RPM.

Online bidding and auction are crucial aspects of the online advertising industry. Conventionally, there is only one slot for ad display and most current studies focus on it. Nowadays, multi-slot display advertising is gradually becoming popular where many ads could be displayed in a list and shown as a whole to users. However, multi-slot display advertising leads to different cost-effectiveness. Advertisers have the incentive to adjust bid prices so as to win the most economical ad positions. In this study, we introduce bid shading into multi-slot display advertising for bid price adjustment with a Multi-task End-to-end Bid Shading~(MEBS) method. We prove the optimality of our method theoretically and examine its performance experimentally. Through extensive offline and online experiments, we demonstrate the effectiveness and efficiency of our method, and we obtain a 7.01% lift in Gross Merchandise Volume, a 7.42% lift in Return on Investment, and a 3.26% lift in ad buy count.

As the self-attention mechanism offers powerful capabilities for capturing sequential relationships, it has become increasingly popular to use it for modeling user behavior sequences in recommender systems. However, the self-attention mechanism has a quadratic computational complexity of O(n^2), as it conducts interactions among all item pairs in the sequence. This can lead to expensive model training and slow inference speeds, which may hinder practical deployment. To this end, we pursue to develop alternative approaches to improve the efficiency of the self-attention mechanism. We observe that the attention scores calculated from each item interacting with other items (including itself) are sparse, indicating that there are limited valuable item pairs (with non-zero attention weight) that contribute to the final output. This motivates us to develop effective strategies for discerning valuable items and computing attention scores solely for these items, thereby minimizing the consumption of unnecessary computations. Herein, we present a novel Progressive Sampling-based Self-Attention (PS-SA) mechanism, which utilizes a learnable progressive sampling strategy to identify the most valuable items. Subsequently, we solely utilize these selected items to produce the final output. Experiments on academic and production datasets demonstrate PS-SA could still achieve promising results while reducing computational costs. It is notable that we have successfully deployed it on Alibaba display advertising system, resulting in a 2.6% CTR and 1.3% RPM increase.

Cascading architecture has been widely adopted in large-scale advertising systems to balance efficiency and effectiveness. In this architecture, the pre-ranking model is expected to be a lightweight approximation of the ranking model, which handles more candidates with strict latency requirements. Due to the gap in model capacity, the pre-ranking and ranking models usually generate inconsistent ranked results, thus hurting the overall system effectiveness. The paradigm of score alignment is proposed to regularize their raw scores to be consistent. However, it suffers from inevitable alignment errors and error amplification by bids when applied in online advertising. To this end, we introduce a consistency-oriented pre-ranking framework for online advertising, which employs a chunk-based sampling module and a plug-and-play rank alignment module to explicitly optimize consistency of ECPM-ranked results. A ΔNDCG-based weighting mechanism is adopted to better distinguish the importance of inter-chunk samples in optimization. Both online and offline experiments have validated the superiority of our framework. When deployed in Taobao display advertising system, it achieves an improvement of up to +12.3% CTR and +5.6% RPM.

Conversion rate (CVR) prediction is one of the core components in online recommender systems, and various approaches have been proposed to obtain accurate and well-calibrated CVR estimation. However, we observe that a well-trained CVR prediction model often performs sub-optimally during sales promotions. This can be largely ascribed to the problem of the data distribution shift, in which the conventional methods no longer work. To this end, we seek to develop alternative modeling techniques for CVR prediction. Observing similar purchase patterns across different promotions, we propose reusing the historical promotion data to capture the promotional conversion patterns. Herein, we propose a novel Historical Data Reuse (HDR) approach that first retrieves historically similar promotion data and then fine-tunes the CVR prediction model with the acquired data for better adaptation to the promotion mode. HDR consists of three components: an automated data retrieval module that seeks similar data from historical promotions, a distribution shift correction module that re-weights the retrieved data for better aligning with the target promotion, and a TransBlock module that quickly fine-tunes the original model for better adaptation to the promotion mode. Experiments conducted with real-world data demonstrate the effectiveness of HDR, as it improves both ranking and calibration metrics to a large extent. HDR has also been deployed on the display advertising system in Alibaba, bringing a lift of 9% RPM and 16% CVR during Double 11 Sales in 2022.

Learning-based ad auctions have increasingly been adopted in online advertising. However, existing approaches neglect externalities, such as the interaction between ads and organic items. In this paper, we propose a general framework, namely Score-Weighted VCG, for designing learning-based ad auctions that account for externalities. The framework decomposes the optimal auction design into two parts: designing a monotone score function and an allocation algorithm, which facilitates data-driven implementation. Theoretical results demonstrate that this framework produces the optimal incentive-compatible and individually rational ad auction under various externality-aware CTR models while being data-efficient and robust. Moreover, we present an approach to implement the proposed framework with a matching-based allocation algorithm. Experiment results on both real-world and synthetic data illustrate the effectiveness of the proposed approach.

Despite the development of ranking optimization techniques, pointwise loss remains the dominating approach for click-through rate prediction. It can be attributed to the calibration ability of the pointwise loss since the prediction can be viewed as the click probability. In practice, a CTR prediction model is also commonly assessed with the ranking ability. To optimize the ranking ability, ranking loss (e.g., pairwise or listwise loss) can be adopted as they usually achieve better rankings than pointwise loss. Previous studies have experimented with a direct combination of the two losses to obtain the benefit from both losses and observed an improved performance. However, previous studies break the meaning of output logit as the click-through rate, which may lead to sub-optimal solutions. To address this issue, we propose an approach that can Jointly optimize the Ranking and Calibration abilities (JRC for short). JRC improves the ranking ability by contrasting the logit value for the sample with different labels and constrains the predicted probability to be a function of the logit subtraction. We further show that JRC consolidates the interpretation of logits, where the logits model the joint distribution. With such an interpretation, we prove that JRC approximately optimizes the contextualized hybrid discriminative-generative objective. Experiments on public and industrial datasets and online A/B testing show that our approach improves both ranking and calibration abilities. Since May 2022, JRC has been deployed on the display advertising platform of Alibaba and has obtained significant performance improvements.

Recently, many E-commerce search models are based on Graph Neural Networks (GNNs). Despite their promising performances, they are (1) lacking proper semantic representation of product contents; (2) less efficient for industry-scale graphs; and (3) less accurate on long-tail queries and cold-start products. To address these problems simultaneously, this paper proposes CC-GNN, a novel Content Collaborative Graph Neural Network. Firstly, CC-GNN enables content phrases to participate explicitly in graph propagation to capture the proper meaning of phrases and semantic drifts. Secondly, CC-GNN presents several efforts towards a more scalable graph learning framework, including efficient graph construction, MetaPath-guided Message Passing, and Difficulty-aware Representation Perturbation for graph contrastive learning. Furthermore, CC-GNN adopts Counterfactual Data Supplement at both supervised and contrastive learning to resolve the long-tail/cold-start problems. Extensive experiments on a real E-commerce dataset of 100-million-scale nodes show that CC-GNN produces significant improvements over existing methods (i.e., more than 10% improvements in terms of several key evaluation metrics for overall, long-tail queries and cold-start products) while reducing computational complexity. The proposed components of CC-GNN can be applied to other models for search and recommendation tasks. Experiments on a public dataset show that applying the proposed components can improve the performance of different recommendation models.

Powered by machine learning techniques, online advertising platforms have launched various automated bidding strategy services to facilitate intelligent decision-making for advertisers. However, advertisers experience heterogeneous advertising environments, and thus the unified bidding strategies widely used in both academia and industry suffer from severe unfairness issues, resulting in significant ad performance disparity among advertisers. In this work, to resolve the unfairness issue and improve the overall system performance, we propose a personalized automated bidding framework, namely PerBid, shifting the classical automated bidding strategy with a unified agent to multiple context-aware agents corresponding to different advertiser clusters. Specifically, we first design an ad campaign profiling network to model dynamic advertising environments. By clustering the advertisers with similar profiles and generating context-aware automated bidding agents for each cluster, we can match advertisers with personalized automated bidding strategies. Experiments conducted on the real-world dataset and online A/B test on Alibaba display advertising platform demonstrate the effectiveness of PerBid in improving overall ad performance and guaranteeing fairness among heterogeneous advertisers.

Recently, Multi-Scenario Learning (MSL) is widely used in recommendation and retrieval systems in the industry because it facilitates transfer learning from different scenarios, mitigating data sparsity and reducing maintenance cost. These efforts produce different MSL paradigms by searching more optimal network structure, such as Auxiliary Network, Expert Network, and Multi-Tower Network. It is intuitive that different scenarios could hold their specific characteristics, activating the user's intents quite differently. In other words, different kinds of auxiliary features would bear varying importance under different scenarios. With more discriminative feature representations refined in a scenario-aware manner, better ranking performance could be easily obtained without expensive search for the optimal network structure. Unfortunately, this simple idea is mainly overlooked but much desired in real-world systems.

A contemporary feed application usually provides blended results of organic items and sponsored items~(ads) to users. Conventionally, ads are exposed at fixed positions. Such a fixed ad exposure strategy is inefficient due to ignoring users' personalized preferences towards ads. To this end,adaptive ad exposure is becoming an appealing strategy to boost the overall performance of the feed. However, existing approaches to implement the adaptive ad exposure strategy suffer from several limitations: 1) they usually fall into sub-optimal solutions because of only focusing on request-level optimization without consideration of the application-level performance and constraints, 2) they neglect the necessity of keeping the game-theoretical properties of ad auctions, and 3) they can hardly be deployed in large-scale applications due to high computational complexity. In this paper, we focus on the application-level performance optimization under hierarchical constraints in feeds and formulate adaptive ad exposure as a Dynamic Knapsack Problem. We propose Hierarchically Constrained Adaptive Ad Exposure~(HCA2E) that possesses the desirable game-theoretical properties, computational efficiency, and performance robustness. Comprehensive offline and online experiments on a leading e-commerce application demonstrate the performance superiority of HCA2E.

We study the search traffic forecasting problem for guaranteed search advertising (GSA) application in e-commerce platforms. The consumers express their purchase intents by posing queries to the e-commerce search engine. GSA is a type of guaranteed delivery (GD) advertising strategy, which forecasts the traffic of search queries, and charges the advertisers according to the predicted volumes of search queries the advertisers willing to buy. We employ the time series forecasting method to make the search traffic prediction. Different from existing time series prediction methods, search queries are semantically meaningful, with semantically similar queries possessing similar time series. And they can be grouped according to the brands or categories they belong to, exhibiting hierarchical structures. To fully take advantage of these characteristics, we design a SemanTic AwaRe Deep hierarchical fOrecasting Model (STARDOM for short) which explores the queries' semantic information and the hierarchical structures formed by the queries. Specifically, to exploit hierarchical structure, we propose a reconciliation learning module. It leverages deep learning model to learn the reconciliation relation between the hierarchical series in the latent space automatically, and forces the coherence constraints through a distill reconciliation loss. To exploit semantic information, we propose a semantic representation module and generate semantic aware series embeddings for queries. Extensive experiments are conducted to confirm the effectiveness of the proposed method.

Extracting expressive visual features is crucial for accurate Click-Through-Rate (CTR) prediction in visual search advertising systems. Current commercial systems use off-the-shelf visual encoders to facilitate fast online service. However, the extracted visual features are coarse-grained and/or biased. In this paper, we present a visual encoding framework for CTR prediction to overcome these problems. The framework is based on contrastive learning which pulls positive pairs closer and pushes negative pairs apart in the visual feature space. To obtain fine-grained visual features, we present contrastive learning supervised by click-through data to fine-tune the visual encoder. To reduce sample selection bias, firstly we train the visual encoder offline by leveraging both unbiased self-supervision and click supervision signals. Secondly, we incorporate a debiasing network in the online CTR predictor to adjust the visual features by contrasting high impression items with selected, low impression items. We deploy the framework in a mobile E-commerce app. Offline experiments on billion-scale datasets and online experiments demonstrate that the proposed framework can make accurate and unbiased predictions.

Product searching is fundamental in online e-commerce systems, it needs to quickly and accurately find the products that users required. Relevance is essential for e-commerce search, which role is avoiding displaying products that do not match search intent and optimizing user experience. Measuring semantic relevance is necessary because distributional biases between search queries and product titles may lead to large lexical differences between relevant textual expressions. Several problems limit the performance of semantic relevance learning, including extremely long-tail product distribution and low-quality labeled data. Recent works attempt to conduct relevance learning through user behaviors. However, noisy user behavior can easily cause inadequately semantic modeling. Therefore, it is valuable but challenging to utilize user behavior in relevance learning. In this paper, we first propose a weakly supervised contrastive learning framework that focuses on how to provide effective semantic supervision and generate reasonable representation. We utilize topology structure information contained in a user behavior heterogeneous graph to design a semantically aware data construction strategy. Besides, we propose a contrastive learning framework suitable for e-commerce scenarios with targeted improvements in data augmentation and training objectives. For relevance calculation, we propose a novel hybrid method that combines fine-tuning and transfer learning. It eliminates the negative impacts caused by distributional bias and guarantees semantic matching capabilities. Extensive experiments and analyses show the promising performance of proposed methods in relevance learning.

Mobile livestreaming ads are becoming a popular approach for brand promotion and product marketing. However, a large number of advertisers fail to achieve their desired advertising performance due to the lack of ad exposure guarantee in the dynamic advertising environment. In this work, we propose a bidding-based ad delivery algorithm for mobile livestreaming ads that can provide advertisers with bidding strategies for optimizing diverse marketing objectives under general ad performance guaranteed constraints, such as ad exposure and cost-efficiency constraints. By modeling the problem as an online integer programming and applying primal-dual theory, we can derive the bidding strategy from solving the optimal dual variables. The initialization of the dual variables is realized through a deep neural network that captures the complex relation between dual variables and dynamic advertising environments. We further propose a control-based bidding algorithm to adjust the dual variables in an online manner based on the real-time advertising performance feedback and constraints. Experiments on a real-world industrial dataset demonstrate the effectiveness of our bidding algorithm in terms of optimizing marketing objectives and guaranteeing ad constraints.

Graph embedding based retrieval has become one of the most popular techniques in the information retrieval community and search engine industry. The classical paradigm mainly relies on the flat Euclidean geometry. In recent years, hyperbolic (negative curvature) and spherical (positive curvature) representation methods have shown their superiority to capture hierarchical and cyclic data structures respectively. However, in industrial scenarios such as e-commerce sponsored search platforms, the large-scale heterogeneous query-item-advertisement interaction graphs often have multiple structures coexisting. Existing methods either only consider a single geometry space, or combine several spaces manually, which are incapable and inflexible to model the complexity and heterogeneity in the real scenario. To tackle this challenge, we present a web-scale Adaptive Mixed-Curvature ADvertisement retrieval system (AM-CAD) to automatically capture the complex and heterogeneous graph structures in non-Euclidean spaces. Specifically, entities are represented in adaptive mixed-curvature spaces, where the types and curvatures of the subspaces are trained to be optimal combinations. Besides, an attentive edge-wise space projector is designed to model the similarities between heterogeneous nodes according to local graph structures and the relation types. Moreover, to deploy AMCAD in Taobao, one of the largest e-commerce platforms with hundreds of million users, we design an efficient two-layer online retrieval framework for the task of graph based advertisement retrieval. Extensive evaluations on real-world datasets and A/B tests on online traffic are conducted to illustrate the effectiveness of the proposed system.

Click-Through Rate (CTR) prediction, estimating the probability of a user clicking on items, plays a key fundamental role in sponsored search. E-commerce platforms display organic search results and advertisements (ads), collectively called items, together as a mixed list. The items displayed around the predicted ad, i.e. external items, may affect the user clicking on the predicted. Previous CTR models assume the user click only relies on the ad itself, which overlooks the effects of external items, referred to as external effects, or externalities. During the advertising prediction, the organic results have been generated by the organic system, while the final displayed ads on multiple ad slots have not been figured out, which leads to two challenges: 1) the predicted (target) ad may win any ad slot, bringing about diverse externalities. 2) external ads are undetermined, resulting in incomplete externalities. Facing the above challenges, inspired by the Transformer, we propose EXternality TRansformer (EXTR) which regards target ad with all slots as query and external items as key&value to model externalities in all exposure situations in parallel. Furthermore, we design a Potential Allocation Generator (PAG) for EXTR, to learn the allocation of potential external ads to complete the externalities. Extensive experimental results on Alibaba datasets demonstrate the effectiveness of externalities in the task of CTR prediction and illustrate that our proposed approach can bring significant profits to the real-world e-commerce platform. EXTR now has been successfully deployed in the online search advertising system in Alibaba, serving the main traffic.

The importance of modeling contextual information within a search session has been widely acknowledged. However, learning representations of multi-query multi-modal (MM) search, in which Mobile Taobao users repeatedly submit textual and visual queries, remains unexplored in literature. Previous work which learns task-specific representations of textual query sessions fails to capture diverse query types and correlations in MM search sessions. This paper presents to represent MM search sessions by heterogeneous graph neural network (HGN). A multi-view contrastive learning framework is proposed to pretrain the HGN, with two views to model different intra-query, inter-query, and inter-modality information diffusion in MM search. Extensive experiments demonstrate that, the pretrained session representation can benefit state-of-the-art baselines on various downstream tasks, such as personalized click prediction, query suggestion, and intent classification.

Learning individual-level treatment effect is a fundamental problem in causal inference and has received increasing attention in many areas, especially in the user growth area which concerns many internet companies. Recently, disentangled representation learning methods that decompose covariates into three latent factors, including instrumental, confounding and adjustment factors, have witnessed great success in treatment effect estimation. However, it remains an open problem how to learn the underlying disentangled factors precisely. Specifically, previous methods fail to obtain independent disentangled factors, which is a necessary condition for identifying treatment effect. In this paper, we propose Disentangled Representations for Counterfactual Regression via Mutual Information Minimization (MIM-DRCFR), which uses a multi-task learning framework to share information when learning the latent factors and incorporates MI minimization learning criteria to ensure the independence of these factors. Extensive experiments including public benchmarks and real-world industrial user growth datasets demonstrate that our method performs much better than state-of-the-art methods.

Recommendation for cold-start users who have very limited data is a canonical challenge in recommender systems. Existing deep recommender systems utilize user content features and behaviors to produce personalized recommendations, yet often face significant performance degradation on cold-start users compared to existing ones due to the following challenges: (1) Cold-start users may have a quite different distribution of features from existing users. (2) The few behaviors of cold-start users are hard to be exploited. In this paper, we propose a recommender system called Cold-Transformer to alleviate these problems. Specifically, we design context-based Embedding Adaption to offset the differences in feature distribution. It transforms the embedding of cold-start users into a warm state that is more like existing ones to represent corresponding user preferences. Furthermore, to exploit the few behaviors of cold-start users and characterize the user context, we propose Label Encoding that models Fused Behaviors of positive and negative feedback simultaneously, which are relatively more sufficient. Last, to perform large-scale industrial recommendations, we keep the two-tower architecture that de-couples user and target item. Extensive experiments on public and industrial datasets show that Cold-Transformer significantly outperforms state-of-the-art methods, including those that are deep coupled and less scalable.

In e-commerce, ad creatives play an important role in effectively delivering product information to users. The purpose of online creative selection is to learn users' preferences for ad creatives, and to select the most appealing design for users to maximize Click-Through Rate (CTR). However, the existing common practices in the industry usually place the creative selection after the ad ranking stage, and thus the optimal creative fails to reflect the influence on the ad ranking stage. To address these issues, we propose a novel Cascade Architecture of Creative Selection (CACS), which is built before the ranking stage to joint optimization of intra-ad creative selection and inter-ad ranking. To improve the efficiency, we design a classic two-tower structure and allow creative embeddings of the creative selection stage to share with the ranking stage. To boost the effectiveness, on the one hand, we propose a soft label list-wise ranking distillation method to distill the ranking knowledge from the ranking stage to guide CACS learning; and on the other hand, we also design an adaptive dropout network to encourage the model to probabilistically ignore ID features in favor of content features to learn multi-modal representations of the creative. Most of all, the ranking model obtains the optimal creative information of each ad from our CACS, and uses all available features to improve the performance of the ranking model. We have launched our solution in Taobao advertising platform and have obtained significant improvements both in offline and online evaluations.

In online advertising, auto-bidding has become an essential tool for advertisers to optimize their preferred ad performance metrics by simply expressing high-level campaign objectives and constraints. Previous works designed auto-bidding tools from the view of single-agent, without modeling the mutual influence between agents. In this paper, we instead consider this problem from a distributed multi-agent perspective, and propose a general \underlineM ulti-\underlineA gent reinforcement learning framework for \underlineA uto-\underlineB idding, namely MAAB, to learn the auto-bidding strategies. First, we investigate the competition and cooperation relation among auto-bidding agents, and propose a temperature-regularized credit assignment to establish a mixed cooperative-competitive paradigm. By carefully making a competition and cooperation trade-off among agents, we can reach an equilibrium state that guarantees not only individual advertiser's utility but also the system performance (i.e., social welfare). Second, to avoid the potential collusion behaviors of bidding low prices underlying the cooperation, we further propose bar agents to set a personalized bidding bar for each agent, and then alleviate the revenue degradation due to the cooperation. Third, to deploy MAAB in the large-scale advertising system with millions of advertisers, we propose a mean-field approach. By grouping advertisers with the same objective as a mean auto-bidding agent, the interactions among the large-scale advertisers are greatly simplified, making it practical to train MAAB efficiently. Extensive experiments on the offline industrial dataset and Alibaba advertising platform demonstrate that our approach outperforms several baseline methods in terms of social welfare and revenue.

Advertisers play an essential role in many e-commerce platforms like Taobao and Amazon. Fulfilling their marketing needs and supporting their business growth is critical to the long-term prosperity of platform economies. However, compared with extensive studies on user modeling such as click-through rate predictions, much less attention has been drawn to advertisers, especially in terms of understanding their diverse demands and performance. Different from user modeling, advertiser modeling generally involves many kinds of tasks (e.g. predictions of advertisers' expenditure, active-rate, or total impressions of promoted products). In addition, major e-commerce platforms often provide multiple marketing scenarios (e.g. Sponsored Search, Display Ads, Live Streaming Ads) while advertisers' behavior tend to be dispersed among many of them. This raises the necessity of multi-task and multi-scenario consideration in comprehensive advertiser modeling, which faces the following challenges: First, one model per scenario or per task simply doesn't scale; Second, it is particularly hard to model new or minor scenarios with limited data samples; Third, inter-scenario correlations are complicated, and may vary given different tasks.

Alleviating the delayed feedback problem is of crucial importance for the conversion rate(CVR) prediction in online advertising. Previous delayed feedback modeling methods using an observation window to balance the trade-off between waiting for accurate labels and consuming fresh feedback. Moreover, to estimate CVR upon the freshly observed but biased distribution with fake negatives, the importance sampling is widely used to reduce the distribution bias. While effective, we argue that previous approaches falsely treat fake negative samples as real negative during the importance weighting and have not fully utilized the observed positive samples, leading to suboptimal performance.

Digital advertising is a critical part of many e-commerce platforms such as Taobao and Amazon. While in recent years a lot of attention has been drawn to the consumer side including canonical problems like ctr/cvr prediction, the advertiser side, which directly serves advertisers by providing them with marketing tools, is now playing a more and more important role. When speaking of sponsored search, bid keyword recommendation is the fundamental service. This paper addresses the problem of keyword matching, the primary step of keyword recommendation. Existing methods for keyword matching merely consider modeling relevance based on a single type of relation among ads and keywords, such as query clicks or text similarity, which neglects rich heterogeneous interactions hidden behind them. To fill this gap, the keyword matching problem faces several challenges including: 1) how to learn enriched and robust embeddings from complex interactions among various types of objects; 2) how to conduct high-quality matching for new ads that usually lack sufficient data.

Nowadays, deep learning models are widely adopted in web-scale applications such as recommender systems, and online advertising. In these applications, embedding learning of categorical features is crucial to the success of deep learning models. In these models, a standard method is that each categorical feature value is assigned a unique embedding vector which can be learned and optimized. Although this method can well capture the characteristics of the categorical features and promise good performance, it can incur a huge memory cost to store the embedding table, especially for those web-scale applications. Such a huge memory cost significantly holds back the effectiveness and usability of EDRMs. In this paper, we propose a binary code based hash embedding method which allows the size of the embedding table to be reduced in arbitrary scale without compromising too much performance. Experimental evaluation results show that one can still achieve 99% performance even if the embedding table size is reduced 1000× smaller than the original one with our proposed method.

Embedding learning for categorical features is crucial for the deep learning-based recommendation models (DLRMs). Each feature value is mapped to an embedding vector via an embedding learning process. Conventional methods configure a fixed and uniform embedding size to all feature values from the same feature field. However, such a configuration is not only sub-optimal for embedding learning but also memory costly. Existing methods that attempt to resolve these problems, either rule-based or neural architecture search (NAS)-based, need extensive efforts on the human design or network training. They are also not flexible in embedding size selection or in warm-start-based applications. In this paper, we propose a novel and effective embedding size selection scheme. Specifically, we design an Adaptively-Masked Twins-based Layer (AMTL) behind the standard embedding layer. AMTL generates a mask vector to mask the undesired dimensions for each embedding vector. The mask vector brings flexibility in selecting the dimensions and the proposed layer can be easily added to either untrained or trained DLRMs. Extensive experimental evaluations show that the proposed scheme outperforms competitive baselines on all the benchmark tasks, and is also memory-efficient, saving 60% memory usage without compromising any performance metrics.

In online display advertising, guaranteed contracts and real-time bidding (RTB) are two major ways to sell impressions for a publisher. For large publishers, simultaneously selling impressions through both guaranteed contracts and in-house RTB has become a popular choice. Generally speaking, a publisher needs to derive an impression allocation strategy between guaranteed contracts and RTB to maximize its overall outcome (e.g., revenue and/or impression quality). However, deriving the optimal strategy is not a trivial task, e.g., the strategy should encourage incentive compatibility in RTB and tackle common challenges in real-world applications such as unstable traffic patterns (e.g., impression volume and bid landscape changing). In this paper, we formulate impression allocation as an auction problem where each guaranteed contract submits virtual bids for individual impressions. With this formulation, we derive the optimal bidding functions for the guaranteed contracts, which result in the optimal impression allocation. In order to address the unstable traffic pattern challenge and achieve the optimal overall outcome, we propose a multi-agent reinforcement learning method to adjust the bids from each guaranteed contract, which is simple, converging efficiently and scalable. The experiments conducted on real-world datasets demonstrate the effectiveness of our method.

Modern online advertising systems inevitably rely on personalization methods, such as click-through rate (CTR) prediction. Recent progress in CTR prediction enjoys the rich representation capabilities of deep learning and achieves great success in large-scale industrial applications. However, these methods can suffer from lack of exploration. Another line of prior work addresses the exploration-exploitation trade-off problem with contextual bandit methods, which are recently less studied in the industry due to the difficulty in extending their flexibility with deep models. In this paper, we propose a novel Deep Uncertainty-Aware Learning (DUAL) method to learn CTR models based on Gaussian processes, which can provide predictive uncertainty estimations while maintaining the flexibility of deep neural networks. DUAL can be easily implemented on existing models and deployed in real-time systems with minimal extra computational overhead. By linking the predictive uncertainty estimation ability of DUAL to well-known bandit algorithms, we further present DUAL-based Ad-ranking strategies to boost up long-term utilities such as the social welfare in advertising systems. Experimental results on several public datasets demonstrate the effectiveness of our methods. Remarkably, an online A/B test deployed in the Alibaba display advertising platform shows an 8.2% social welfare improvement and an 8.0% revenue lift.

Bid optimization for online advertising from single advertiser's perspective has been thoroughly investigated in both academic research and industrial practice. However, existing work typically assume competitors do not change their bids, i.e., the wining price is fixed, leading to poor performance of the derived solution. Although a few studies use multi-agent reinforcement learning to set up a cooperative game, they still suffer the following drawbacks: (1) They fail to avoid collusion solutions where all the advertisers involved in an auction collude to bid an extremely low price on purpose. (2) Previous works cannot well handle the underlying complex bidding environment, leading to poor model convergence. This problem could be amplified when handling multiple objectives of advertisers which are practical demands but not considered by previous work. In this paper, we propose a novel multi-objective cooperative bid optimization formulation called Multi-Agent Cooperative bidding Games (MACG). MACG sets up a carefully designed multi-objective optimization framework where different objectives of advertisers are incorporated. A global objective to maximize the overall profit of all advertisements is added in order to encourage better cooperation and also to protect self-bidding advertisers. To avoid collusion, we also introduce an extra platform revenue constraint. We analyze the optimal functional form of the bidding formula theoretically and design a policy network accordingly to generate auction-level bids. Then we design an efficient multi-agent evolutionary strategy for model optimization. Evolutionary strategy does not need to model the underlying environment explicitly and is more suitable for bid optimization. Offline experiments and online A/B tests conducted on the Taobao platform indicate both single advertiser's objective and global profit have been significantly improved compared to state-of-art methods.

In online display advertising, advertisers usually participate in real-time bidding to acquire ad impression opportunities. In most advertising platforms, a typical impression acquiring demand of advertisers is to maximize the sum value of winning impressions under budget and some key performance indicators constraints, (e.g. maximizing clicks with the constraints of budget and cost per click upper bound). The demand can be various in value type (e.g. ad exposure/click), constraint type (e.g. cost per unit value) and constraint number. Existing works usually focus on a specific demand or hardly achieve the optimum. In this paper, we formulate the demand as a constrained bidding problem, and deduce a unified optimal bidding function on behalf of an advertiser. The optimal bidding function facilitates an advertiser calculating bids for all impressions with only m parameters, where m is the constraint number. However, in real application, it is non-trivial to determine the parameters due to the non-stationary auction environment. We further propose a reinforcement learning (RL) method to dynamically adjust parameters to achieve the optimum, whose converging efficiency is significantly boosted by the recursive optimization property in our formulation. We name the formulation and the RL method, together, as Unified Solution to Constrained Bidding (USCB). USCB is verified to be effective on industrial datasets and is deployed in Alibaba display advertising platform.

In e-commerce advertising, it is crucial to jointly consider various performance metrics, e.g., user experience, advertiser utility, and platform revenue. Traditional auction mechanisms, such as GSP and VCG auctions, can be suboptimal due to their fixed allocation rules to optimize a single performance metric (e.g., revenue or social welfare). Recently, data-driven auctions, learned directly from auction outcomes to optimize multiple performance metrics, have attracted increasing research interests. However, the procedure of auction mechanisms involves various discrete calculation operations, making it challenging to be compatible with continuous optimization pipelines in machine learning. In this paper, we design Deep Neural Auctions (DNAs) to enable end-to-end auction learning by proposing a differentiable model to relax the discrete sorting operation, a key component in auctions. We optimize the performance metrics by developing deep models to efficiently extract contexts from auctions, providing rich features for auction design. We further integrate the game theoretical conditions within the model design, to guarantee the stability of the auctions. DNAs have been successfully deployed in the e-commerce advertising system at Taobao. Experimental evaluation results on both large-scale data set as well as online A/B test demonstrated that DNAs significantly outperformed other mechanisms widely adopted in industry.

Cross features play an important role in click-through rate (CTR) prediction. Most of the existing methods adopt a DNN-based model to capture the cross features in an implicit manner. These implicit methods may lead to a sub-optimized performance due to the limitation in explicit semantic modeling. Although traditional statistical explicit semantic cross features can address the problem in these implicit methods, it still suffers from some challenges, including lack of generalization and expensive memory cost. Few works focus on tackling these challenges. In this paper, we take the first step in learning the explicit semantic cross features and propose Pre-trained Cross Feature learning Graph Neural Networks (PCF-GNN), a GNN based pre-trained model aiming at generating cross features in an explicit fashion. Extensive experiments are conducted on both public and industrial datasets, where PCF-GNN shows competence in both performance and memory-efficiency in various tasks.

In real-world search, recommendation, and advertising systems, the multi-stage ranking architecture is commonly adopted. Such architecture usually consists of matching, pre-ranking, ranking, and re-ranking stages. In the pre-ranking stage, vector-product based models with representation-focused architecture are commonly adopted to account for system efficiency. However, it brings a significant loss to the effectiveness of the system. In this paper, a novel pre-ranking approach is proposed which supports complicated models with interaction-focused architecture. It achieves a better tradeoff between effectiveness and efficiency by utilizing the proposed learnable Feature Selection method based on feature Complexity and variational Dropout (FSCD). Evaluations in a real-world e-commerce sponsored search system for a search engine demonstrate that utilizing the proposed pre-ranking, the effectiveness of the system is significantly improved. Moreover, compared to the systems with conventional pre-ranking models, an identical amount of computational resource is consumed.

In e-commerce advertising, the ad platform usually relies on auction mechanisms to optimize different performance metrics, such as user experience, advertiser utility, and platform revenue. However, most of the state-of-the-art auction mechanisms only focus on optimizing a single performance metric, e.g., either social welfare or revenue, and are not suitable for e-commerce advertising with various, dynamic, difficult to estimate, and even conflicting performance metrics. In this paper, we propose a new mechanism called Deep GSP auction, which leverages deep learning to design new rank score functions within the celebrated GSP auction framework. These new rank score functions are implemented via deep neural network models under the constraints of monotone allocation and smooth transition. The requirement of monotone allocation ensures Deep GSP auction nice game theoretical properties, while the requirement of smooth transition guarantees the advertiser utilities would not fluctuate too much when the auction mechanism switches among candidate mechanisms to achieve different optimization objectives. We deployed the proposed mechanisms in a leading e-commerce ad platform and conducted comprehensive experimental evaluations with both offline simulations and online A/B tests. The results demonstrated the effectiveness of the Deep GSP auction compared to the state-of-the-art auction mechanisms.

For e-commerce platforms such as Taobao and Amazon, advertisers play an important role in the entire digital ecosystem: their behaviors explicitly influence users' browsing and shopping experience; more importantly, advertiser's expenditure on advertising constitutes a primary source of platform revenue. Therefore, providing better services for advertisers is essential for the long-term prosperity for e-commerce platforms. To achieve this goal, the ad platform needs to have an in-depth understanding of advertisers in terms of both their marketing intents and satisfaction over the advertising performance, based on which further optimization could be carried out to service the advertisers in the correct direction. In this paper, we propose a novel Deep Satisfaction Prediction Network (DSPN), which models advertiser intent and satisfaction simultaneously. It employs a two-stage network structure where advertiser intent vector and satisfaction are jointly learned by considering the features of advertiser's action information and advertising performance indicators. Experiments on an Alibaba advertisement dataset and online evaluations show that our proposed DSPN outperforms state-of-the-art baselines and has stable performance in terms of AUC in the online environment. Further analyses show that DSPN not only predicts advertisers' satisfaction accurately but also learns an explainable advertiser intent, revealing the opportunities to optimize the advertising performance further.

To drive purchase in online advertising, it is of the advertiser's great interest to optimize the sequential advertising strategy whose performance and interpretability are both important. The lack of interpretability in existing deep reinforcement learning methods makes it not easy to understand, diagnose and further optimize the strategy.In this paper, we propose our Deep Intents Sequential Advertising (DISA) method to address these issues. The key part of interpretability is to understand a consumer's purchase intent which is, however, unobservable (called hidden states). In this paper, we model this intention as a latent variable and formulate the problem as a Partially Observable Markov Decision Process (POMDP) where the underlying intents are inferred based on the observable behaviors. Large-scale industrial offline and online experiments demonstrate our method's superior performance over several baselines. The inferred hidden states are analyzed, and the results prove the rationality of our inference.

Position bias is a critical problem in information retrieval when dealing with implicit yet biased user feedback data. Unbiased ranking methods typically rely on causality models and debias the user feedback through inverse propensity weighting. While practical, these methods still suffer from two major problems. First, when infer a user click, the impact of the contextual information, such as documents that have been examined, is often ignored. Second, only the position bias is considered but other issues resulted from user browsing behaviors are overlooked. In this paper, we propose an end-to-end Deep Recurrent Survival Ranking (DRSR), a unified framework to jointly model user's various behaviors, to (i) consider the rich contextual information in the ranking list; and (ii) address the hidden issues underlying user behaviors, i.e., to mine observe pattern in queries without any click (non-click queries), and to model tracking logs which cannot truly reflect the user browsing intents (untrusted observation). Specifically, we adopt a recurrent neural network to model the contextual information and estimates the conditional likelihood of user feedback at each position. We then incorporate survival analysis techniques with the probability chain rule to mathematically recover the unbiased joint probability of one user's various behaviors. DRSR can be easily incorporated with both point-wise and pair-wise learning objectives. The extensive experiments over two large-scale industrial datasets demonstrate the significant performance gains of our model comparing with the state-of-the-arts.

Most e-commerce product feeds provide blended results of advertised products and recommended products to consumers. The underlying advertising and recommendation platforms share similar if not exactly the same set of candidate products. Consumers' behaviors on the advertised results constitute part of the recommendation model's training data and therefore can influence the recommended results. We refer to this process as Leverage. Considering this mechanism, we propose a novel perspective that advertisers can strategically bid through the advertising platform to optimize their recommended organic traffic. By analyzing the real-world data, we first explain the principles of Leverage mechanism, i.e., the dynamic models of Leverage. Then we introduce a novel Leverage optimization problem and formulate it with a Markov Decision Process. To deal with the sample complexity challenge in model-free reinforcement learning, we propose a novel Hybrid Training Leverage Bidding (HTLB) algorithm which combines the real-world samples and the emulator-generated samples to boost the learning speed and stability. Our offline experiments as well as the results from the online deployment demonstrate the superior performance of our approach.

In E-commerce advertising, where product recommendations and product ads are presented to users simultaneously, the traditional setting is to display ads at fixed positions. However, under such a setting, the advertising system loses the flexibility to control the number and positions of ads, resulting in sub-optimal platform revenue and user experience. Consequently, major e-commerce platforms (e.g., Taobao.com) have begun to consider more flexible ways to display ads. In this paper, we investigate the problem of advertising with adaptive exposure: can we dynamically determine the number and positions of ads for each user visit under certain business constraints so that the platform revenue can be increased? More specifically, we consider two types of constraints: request-level constraint ensures user experience for each user visit, and platform-level constraint controls the overall platform monetization rate. We model this problem as a Constrained Markov Decision Process with per-state constraint (psCMDP) and propose a constrained two-level reinforcement learning approach to decompose the original problem into two relatively independent sub-problems. To accelerate policy learning, we also devise a constrained hindsight experience replay mechanism. Experimental evaluations on industry-scale real-world datasets demonstrate the merits of our approach in both obtaining higher revenue under the constraints and the effectiveness of the constrained hindsight experience replay mechanism.

Real-Time Bidding (RTB) is an important paradigm in display advertising, where advertisers utilize extended information and algorithms served by Demand Side Platforms (DSPs) to improve advertising performance. A common problem for DSPs is to help advertisers gain as much value as possible with budget constraints. However, advertisers would routinely add certain key performance indicator (KPI) constraints that the advertising campaign must meet due to practical reasons. In this paper, we study the common case where advertisers aim to maximize the quantity of conversions, and set cost-per-click (CPC) as a KPI constraint. We convert such a problem into a linear programming problem and leverage the primal-dual method to derive the optimal bidding strategy. To address the applicability issue, we propose a feedback control-based solution and devise the multivariable control system. The empirical study based on real-word data from Taobao.com verifies the effectiveness and superiority of our approach compared with the state of the art in the industry practices.

User response prediction, which models the user preference w.r.t. the presented items, plays a key role in online services. With two-decade rapid development, nowadays the cumulated user behavior sequences on mature Internet service platforms have become extremely long since the user's first registration. Each user not only has intrinsic tastes, but also keeps changing her personal interests during lifetime. Hence, it is challenging to handle such lifelong sequential modeling for each individual user. Existing methodologies for sequential modeling are only capable of dealing with relatively recent user behaviors, which leaves huge space for modeling long-term especially lifelong sequential patterns to facilitate user modeling. Moreover, one user's behavior may be accounted for various previous behaviors within her whole online activity history, i.e., long-term dependency with multi-scale sequential patterns. In order to tackle these challenges, in this paper, we propose a Hierarchical Periodic Memory Network for lifelong sequential modeling with personalized memorization of sequential patterns for each user. The model also adopts a hierarchical and periodical updating mechanism to capture multi-scale sequential patterns of user interests while supporting the evolving user behavior logs. The experimental results over three large-scale real-world datasets have demonstrated the advantages of our proposed model with significant improvement in user response prediction performance against the state-of-the-arts.

Real-time bidding (RTB) is an important mechanism in online display advertising, where a proper bid for each page view plays an essential role for good marketing results. Budget constrained bidding is a typical scenario in RTB where the advertisers hope to maximize the total value of the winning impressions under a pre-set budget constraint. However, the optimal bidding strategy is hard to be derived due to the complexity and volatility of the auction environment. To address these challenges, in this paper, we formulate budget constrained bidding as a Markov Decision Process and propose a model-free reinforcement learning framework to resolve the optimization problem. Our analysis shows that the immediate reward from environment is misleading under a critical resource constraint. Therefore, we innovate a reward function design methodology for the reinforcement learning problems with constraints. Based on the new reward design, we employ a deep neural network to learn the appropriate reward so that the optimal policy can be learned effectively. Different from the prior model-based work, which suffers from the scalability problem, our framework is easy to be deployed in large-scale industrial applications. The experimental evaluations demonstrate the effectiveness of our framework on large-scale real datasets.

Click-through rate (CTR) prediction is a critical task in online display advertising. The data involved in CTR prediction are typically multi-field categorical data, i.e., every feature is categorical and belongs to one and only one field. One of the interesting characteristics of such data is that features from one field often interact differently with features from different other fields. Recently, Field-aware Factorization Machines (FFMs) have been among the best performing models for CTR prediction by explicitly modeling such difference. However, the number of parameters in FFMs is in the order of feature number times field number, which is unacceptable in the real-world production systems. In this paper, we propose Field-weighted Factorization Machines (FwFMs) to model the different feature interactions between different fields in a much more memory-efficient way. Our experimental evaluations show that FwFMs can achieve competitive prediction performance with only as few as 4% parameters of FFMs. When using the same number of parameters, FwFMs can bring 0.92% and 0.47% AUC lift over FFMs on two real CTR prediction data sets.

In real-time display advertising, ad slots are sold per impression via an auction mechanism. For an advertiser, the campaign information is incomplete --- the user responses (e.g, clicks or conversions) and the market price of each ad impression are observed only if the advertiser's bid had won the corresponding ad auction. The predictions, such as bid landscape forecasting, click-through rate (CTR) estimation, and bid optimisation, are all operated in the pre-bid stage with full-volume bid request data. However, the training data is gathered in the post-bid stage with a strong bias towards the winning impressions. A common solution for learning over such censored data is to reweight data instances to correct the discrepancy between training and prediction. However, little study has been done on how to obtain the weights independent of previous bidding strategies and consequently integrate them into the final CTR prediction and bid generation steps. In this paper, we formulate CTR estimation and bid optimisation under such censored auction data. Derived from a survival model, we show that historic bid information is naturally incorporated to produce Bid-aware Gradient Descents (BGD) which controls both the importance and the direction of the gradient to achieve unbiased learning. The empirical study based on two large-scale real-world datasets demonstrates remarkable performance gains from our solution. The learning framework has been deployed on Yahoo!'s real-time bidding platform and provided 2.97% AUC lift for CTR estimation and 9.30% eCPC drop for bid optimisation in an online A/B test.

In targeted online advertising, advertisers look for maximizing campaign performance under delivery constraint within budget schedule. Most of the advertisers typically prefer to impose the delivery constraint to spend budget smoothly over the time in order to reach a wider range of audiences and have a sustainable impact. Since lots of impressions are traded through public auctions for online advertising today, the liquidity makes price elasticity and bid landscape between demand and supply change quite dynamically. Therefore, it is challenging to perform smooth pacing control and maximize campaign performance simultaneously. In this paper, we propose a smart pacing approach in which the delivery pace of each campaign is learned from both offline and online data to achieve smooth delivery and optimal performance goals. The implementation of the proposed approach in a real DSP system is also presented. Experimental evaluations on both real online ad campaigns and offline simulations show that our approach can effectively improve campaign performance and achieve delivery goals.

K-anonymity is a simple yet practical mechanismto protect privacy against attacks of re-identifying individuals by joining multiple public data sources. All existing methods achieving k-anonymity assume implicitly that the data objects to be anonymized are given once and fixed. However, in many applications, the real world data sources are dynamic. In this paper, we investigate the problem of maintaining k-anonymity against incremental updates, and propose a simple yet effective solution. We analyze how inferences from multiple releases may temper the k-anonymity of data, and propose the monotonic incremental anonymization property. The general idea is to progressively and consistently reduce the generalization granularity as incremental updates arrive. Our new approach guarantees the k-anonymity on each release, and also on the inferred table using multiple releases. At the same time, our new approach utilizes the more and more accumulated data to reduce the information loss.

Privacy becomes a more and more serious concern in applications involving microdata. Recently, efficient anonymization has attracted much research work. Most of the previous methods use global recoding, which maps the domains of the quasi-identifier attributes to generalized or changed values. However, global recoding may not always achieve effective anonymization in terms of discernability and query answering accuracy using the anonymized data. Moreover, anonymized data is often for analysis. As well accepted in many analytical applications, different attributes in a data set may have different utility in the analysis. The utility of attributes has not been considered in the previous methods.In this paper, we study the problem of utility-based anonymization. First, we propose a simple framework to specify utility of attributes. The framework covers both numeric and categorical data. Second, we develop two simple yet efficient heuristic local recoding methods for utility-based anonymization. Our extensive performance study using both real data sets and synthetic data sets shows that our methods outperform the state-of-the-art multidimensional global recoding methods in both discernability and query answering accuracy. Furthermore, our utility-based method can boost the quality of analysis using the anonymized data.