Industrial generative recommendation systems operating over discrete Semantic IDs (SIDs) are largely behavior-driven, and thus struggle to proactively activate latent demand before explicit user signals emerge, leading to intent cold-start. To address this, we propose RIGER (Reasoning-grounded Intent injection for GE nerative Recommendation), a deployable two-stage framework that integrates offline large language model (LLM) reasoning into an online generative recommender under strict latency constraints. Offline, to ensure scalable deployment, we distill the latent-intent inference capability of a strong LLM into a lightweight forecasting model using an automated data curation pipeline---leveraging judge-guided prompt calibration and future-query-guided rejection filtering. Online, to bridge the representation mismatch between free-form textual intents and the discrete SID token space, predicted intents are converted into SID-native tokens through a behavior-grounded mapping and injected into the deployed decoder-only retrieval backbone. We further fine-tune the model with beam-aware GRPO, introducing a hierarchical intent-alignment exploration reward in SID space while preserving exploitation behavior through KL regularization. Offline evaluations demonstrate a substantial increase in intent-aligned density and diversity with only a marginal reduction in hindsight recall, indicating that RIGER effectively enhances proactive intent exploration while preserving its capability to exploit historical behaviors. In a large-scale e-commerce display advertising system, RIGER improves clicks by 1.6% and advertiser spend by 1.3%.

Click-through rate (CTR) prediction is a pivotal component in large-scale industrial systems. Historically, CTR prediction paradigms have been confined to monolithic architectures governed by a single-policy optimization process. However, such isolated learning paths lack the intrinsic evolutionary mechanisms necessary for optimal convergence. Without policy diversity and internal competition, models tend to get trapped in local optima as performance reaches saturation, hindering further breakthroughs in modeling capacity. In this paper, we propose DPEO (Dynamic Preference Evolution Optimization), a co-evolutionary framework that transforms CTR modeling into a dynamic policy contention task. DPEO decouples the monolithic architecture into dual sub-learners to induce policy diversity, constructing an internal preference landscape without external rewards. A performance-driven Role Arbiter then dynamically designates the superior sub-learner as the Reference Policy and the other sub-learner as the Target Policy per batch, driving continuous model evolution. Through an asymmetric gradient flow, the target policy is optimized to surpass the reference policy in both probability and logit spaces. This process drives a co-evolution, enabling the sub-learners to serve as alternating evolutionary benchmarks and 'self-evolve' toward the global optimum. Extensive experiments on public benchmarks and a massive industrial dataset with over 10 billion samples demonstrate that DPEO significantly outperforms state-of-the-art models.

Generative Recommendation (GenRec) has emerged as a significant evolutionary direction in the field of recommendation systems in recent years. However, during the reinforcement learning (RL) alignment stage of end-to-end GenRec, a core challenge is that the low signal-to-noise ratio (SNR) of feedback signals triggers reward hacking, which in turn leads to severe distributional drift. Massive click noise and sparse rewards result in highly unstable policy gradients, making it difficult to balance reward optimization and generative stability. To address this, we propose AIPO (Adaptive Anchored Intent-aware Policy Optimization) framework. AIPO synergistically enhances both optimization stability and performance from both data and prior anchoring perspectives. First, it introduces an intent-aware asymmetric resampling mechanism to purify high-confidence conversion intent data, thereby amplifying the gradient contribution of high-value paths. Second, it introduces a prior anchoring mechanism, which dynamically regulates its intensity through a barrier mapping based on the degree of policy distributional drift, mitigating distributional drift and reward hacking caused by low-SNR data. Offline experiments on both public benchmarks and JD's industrial datasets validated the superior performance of AIPO.

Auto-bidding systems are widely used in advertising to automatically determine bid values under constraints such as total budget and Return-on-Spend (RoS) targets. Existing works often assume that the value of an ad impression, such as the conversion rate, is known. This paper considers the more realistic scenario where the true value is unknown. We propose a novel method that uses conformal prediction to quantify the uncertainty of these values based on machine learning methods trained on historical bidding data with contextual features, without assuming the data are i.i.d. This approach is compatible with current industry systems that use machine learning to predict values. Building on prediction intervals, we introduce an adjusted value estimator derived from machine learning predictions, and show that it provides performance guarantees without requiring knowledge of the true value. We apply this method to enhance existing auto-bidding algorithms with budget and RoS constraints, and establish theoretical guarantees for achieving high reward while keeping RoS violations low. Empirical results on both simulated and real-world industrial datasets demonstrate that our approach improves performance while maintaining computational efficiency.

The ranking stage serves as the central optimization and allocation hub in advertising systems, governing economic value distribution through eCPM and orchestrating the user-centric blending of organic and advertising content. Prevailing ranking models often rely on fragmented modules and hand-crafted features, limiting their ability to interpret complex user intent. This challenge is further amplified in location-based services such as food delivery, where user decisions are shaped by dynamic spatial, temporal, and individual contexts. To address these limitations, we propose a novel generative framework that reframes ranking as a context comprehension task, modeling heterogeneous signals in a unified architecture. Our architecture consists of two core components: the Generative Contextual Encoder (GCE) and the Generative Contextual Fusion (GCF). The GCE comprises three specialized modules: a Personalized Context Enhancer (PCE) for user-specific modeling, a Collective Context Enhancer (CCE) for group-level patterns, and a Dynamic Context Enhancer (DCE) for real-time situational adaptation. The GCF module then seamlessly integrates these contextual representations through low-rank adaptation. Extensive experiments confirm that our method achieves significant gains in critical business metrics, including click-through rate and platform revenue. We have successfully deployed our method on a large-scale food delivery advertising platform, demonstrating its substantial practical impact. This work pioneers a new perspective on generative recommendation and highlights its practical potential in industrial advertising systems.

Trigger-Induced Recommendation (TIR), which aims to predict user interest based on a trigger item, has gained considerable traction on e-commerce platforms. Current TIR methods typically analyze user intent by integrating explicit interest in the trigger item and implicit interest derived from user historical behaviors. However, these methods often overlook the contextual information and occurring scenarios related to the trigger, resulting in an undue emphasis on isolated trigger items and a consequently restrictive understanding of users' short-term intentions. To address these challenges, we propose a novel scenario-aware multi-trigger induction method featuring three key enhancements: (1) The Context Modeling Network learns contextual information associated with the trigger during the request, improving the understanding of users' real intentions regarding the trigger item; (2) The Multi-Trigger Learning Network introduces user latent triggers from various scenarios to uncover users' potential external preferences; (3) The Scenario Induction Network captures the characteristics of the scenarios in which triggers occur and performs induction to yield scenario-aware user intentions prediction. We validate our approach through experiments on multiple industrial datasets, demonstrating the model's effectiveness. Furthermore, we have integrated the model into an online advertising system, achieving a 5.46% improvement in Click-Through Rate (CTR).

E-commerce has become an integral part of daily life, and the ability to effectively retrieve items relevant to a user's query is crucial for enhancing the shopping experience. Embedding-based retrieval (EBR) has proven to be an effective approach in industrial e-commerce search systems. This method involves training models to generate high-quality representations of queries and items, followed by the use of efficient approximate nearest neighbor (ANN) search techniques to find relevant items. However, current EBR methods face several critical limitations: (1) multiple EBR branches often retrieve overlapping or redundant result sets; (2) allocation and utilization of computational resources remain suboptimal, hindering performance; (3) the differentiation modeling of features is somewhat neglected, which restricts the system's ability to retrieve diverse and representative results. These issues hinder the retrieval performance of online search systems. In this paper, we introduce a novel e-commerce search framework called the Uniform Embedding-based Retrieval Framework (UniERF). This framework is meticulously designed to incorporate diverse samples for joint model training, enabling the model to effectively leverage both the semantic information of queries and the personalized features of different users. Extensive offline and online experiments demonstrate that UniERF surpasses baseline methods across various evaluation metrics. UniERF has been successfully implemented in the existing retrieval system at JD.COM, a renowned online shopping website.

Accurate click-through rate (CTR) prediction is critical for online advertising, relying on regular features like browsing history and demographics and post-event features such as exposed position and detailed page behaviors. However, post-event features, unavailable during inference, often face training-inference inconsistency and low coverage issues, especially post-click features like dwell time that are available only for clicked items. To address these challenges, we propose Causal Optimal Transport (COT), a novel framework that (1) generates pseudo post-click features via semi-supervised pseudo-labeling (2) causally generates accurate feature distributions using a Causal Distribution Shaper (CDS), and (3) refines generated features through optimal transport to minimize distributional divergence, facilitating further knowledge transfer. Experiments on real-world data confirm COT's superiority and practical efficacy in enhancing CTR prediction via improved user interest modeling and bias mitigation. Theoretical guarantees underpin the framework's robustness.

QQ and Wechat are the two largest instant messaging & social networks in China. Tencent Social Ads is the advertising platform for both Wechat and QQ, serving over 10B page views per day for several hundred million users. We strive to understand as much as possible on our users' characteristics, so as to serve the best personalized ads for them. The rich user behaviors on Tencent's diverse products lay a foundation in our user profiles on many dimensions, including demographics, interests, intents, transactions, physical locations, and devices, etc. In this talk, we will share our experience in large scale user data mining based on online social activities. We will discuss the challenges we face and the solutions we have devised so far. Some demographics data are obtained from user input, and thus would have gaps in both accuracy and coverage. We discuss the techniques in calibrating and verifying these data. We infer user interests from their social behaviors. For example, most QQ groups are not labelled properly, but by applying a large-scale topic model on the QQ memberships, we can effectively classify most QQ groups into an interest taxonomy. We also infer user interests from user's physical location check-ins and uploaded photos. User data can be collected from many diverse sources, including behaviors in various Tencent products, click and conversion in ad platform, and even seed customers collected by advertisers. We'll discuss the systems to merge these diverse data to provide a coherent view for our advertisers. High quality user labels are usually sparse. We implemented an algorithm for advertisers to reach more potential customers through user similarity computation based on user features as well as social graph inferences. We'll describe the system's contributions to ads quality. Top advertisers demand rich audience targeting solutions in combination of their own customer data, Tencent data, and possibly 3rd-party data. We'll discuss the data exchange platform that can facilitate collaborative applications with 3rd-party DSPs and DMPs.

Latent Dirichlet allocation (LDA) is a popular topic modeling technique in academia but less so in industry, especially in large-scale applications involving search engine and online advertising systems. A main underlying reason is that the topic models used have been too small in scale to be useful; for example, some of the largest LDA models reported in literature have up to 10 3 topics, which difficultly cover the long-tail semantic word sets. In this article, we show that the number of topics is a key factor that can significantly boost the utility of topic-modeling systems. In particular, we show that a “big” LDA model with at least 10 5 topics inferred from 10 9 search queries can achieve a significant improvement on industrial search engine and online advertising systems, both of which serve hundreds of millions of users. We develop a novel distributed system called Peacock to learn big LDA models from big data. The main features of Peacock include hierarchical distributed architecture, real-time prediction, and topic de-duplication. We empirically demonstrate that the Peacock system is capable of providing significant benefits via highly scalable LDA topic models for several industrial applications.