The need to safeguard data privacy and adhere to regulations such as GDPR creates data silos and has prompted the emergence and widespread adoption of techniques for distributed databases. To effectively explore the value of data across multiple organizations, techniques for data management, data analysis and data functionality from distributed databases have been proposed. Recently, Vertical Federated Learning (VFL) has become a solution with growing interests, which enables collaborative model training when data features are partitioned into multiple parts and are held by different parties. However, typical VFL methods heavily rely on private set intersection (PSI) to align data before training and only utilize aligned data for training. In this work, we provide a theoretical analysis to show that unaligned data actually contains valuable and rich features, and a thoughtful design that harnesses the potential of unaligned samples to significantly improve the performance of VFL models. Regrettably, many existing methods simply discard unaligned data, resulting in an irrecoverable loss of performance. To address this data sacrifice problem, we introduce the concept of data mixture, which enables the utilization of both aligned and unaligned data during training. Building upon the data mixture idea, we present FedMix, the first on-the-fly and distribution-agnostic framework designed to boost the performance of VFL models by leveraging unaligned data. A data seasoning approach is also designed to utilize auxiliary data lacking label information. Evaluations on diverse datasets under different settings demonstrate the effectiveness of the proposed FedMix compared with various SOTA approaches. FedMix achieves up to 15% model performance improvement and 30.5 hours time cost reduction.

Real-time Bidding (RTB) advertisers wish to know in advance the expected cost and yield of ad campaigns to avoid trial-and-error expenses.However, Campaign Performance Forecasting (CPF), a sequence modeling task involving tens of thousands of ad auctions, poses challenges of evolving user interest, auction representation, and long context, making coarse-grained and static-modeling methods sub-optimal.We propose AdVance, a time-aware framework that integrates local auction-level and global campaign-level modeling.User preference and fatigue are disentangled using a timepositioned sequence of clicked items and a concise vector of all displayed items.Cross-attention, conditioned on the fatigue vector, captures the dynamics of user interest toward each candidate ad.Bidders compete with each other, presenting a complete graph similar to the self-attention mechanism.Hence, we employ a Transformer Encoder to compress each auction into embedding by solving auxiliary tasks.These sequential embeddings are then summarized by a conditional state space model (SSM) to comprehend long-range dependencies while maintaining global linear complexity.Considering the irregular time intervals between auctions, we

Conversion rate (CVR) prediction is essential in recommender systems, facilitating precise matching between recommended items and users’ preferences. However, the sample selection bias (SSB) and data sparsity (DS) issues pose challenges to accurate prediction. Existing works have proposed the click-through and conversion rate (CTCVR) prediction task which models samples from exposure to ``click and conversion" in entire space and incorporates multi-task learning. This approach has shown efficacy in mitigating these challenges. Nevertheless, it intensifies the false negative sample (FNS) problem. To be more specific, the CTCVR task implicitly treats all the CVR labels of non-click samples as negative, overlooking the possibility that some samples might convert if clicked. This oversight can negatively impact CVR model performance, as empirical analysis has confirmed. To this end, we advocate for discarding the CTCVR task and proposing a Non-click samples Improved Semi-supErvised (NISE) method for conversion rate prediction, where the non-click samples are treated as unlabeled. Our approach aims to predict their probabilities of conversion if clicked, utilizing these predictions as pseudo-labels for further model training. This strategy can help alleviate the FNS problem, and direct modeling of the CVR task across the entire space also mitigates the SSB and DS challenges. Additionally, we conduct multi-task learning by introducing an auxiliary click-through rate prediction task, thereby enhancing embedding layer representations. Our approach is applicable to various multi-task architectures. Comprehensive experiments are conducted on both public and production datasets, demonstrating the superiority of our proposed method in mitigating the FNS challenge and improving the CVR estimation. The implementation code is available at https://github.com/Hjh233/NISE.

The diverse advertiser demands (brand effects or immediate outcomes) lead to distinct selling (pre-agreed volumes with an under-delivery penalty or compete per auction) and pricing (fixed prices or varying bids) patterns in Guaranteed delivery (GD) and real-time bidding (RTB) advertising. This necessitates fair impression allocation to unify the two markets for promoting ad content diversity and overall revenue. Existing approaches often deprive RTB ads of equal exposure opportunities by prioritizing GD ads, and coarse-grained methods are inferior to 1) Ambiguous reward due to varied objectives and constraints of GD fulfillment and RTB utility, hindering measurement of each allocation's contribution to the global interests; 2) Intensified competition by the coexistence of GD and RTB ads, complicating their mutual relationships; 3) Policy degradation caused by evolving user traffic and bid landscape, requiring adaptivity to distribution shifts.

User impression forecasting underpins various commercial activities, from long-term strategic decisions to short-term automated operations. As a representative that involves both kinds, the highly profitable Guaranteed Delivery (GD) advertising focuses mainly on promoting brand effect by allowing advertisers to order target impressions weeksin advance and get allocatedonline at the scheduled time. Such a business mode naturally incurs three issues making existing solutions inferior: 1) Timescale-granularity dilemma of coherently supporting the sales of day-level impressions of the distant future and the corresponding fine-grained allocation in real-time. 2) High dimensionality due to the Cartesian product of user attribute combinations. 3) Stability-plasticity dilemma of instant adaptation to emerging patterns of temporal dependency withoutcatastrophic forgetting of repeated ones facing the non-stationary traffic.

In online display advertising, guaranteed delivery (GD) ads and real-time bidding (RTB) are two main ways to sell impressions for a publisher. While RTB has gained increasing popularity, there is still a proportion of revenue generated from GD ads [1]. Existing mainstream impression allocation models deal with the two delivery ways separately, failing to achieve optimal allocation for multi-objective under multi-constraints, e.g., maximizing gross merchandise volume pre mille (GPM) and revenue per mille (RPM), thus limiting the overall revenue for both the publisher and advertisers. To solve the above problems, we propose a unified guaranteed impression allocation framework to optimally allocate impressions for both GD ads and RTB ads simultaneously. Specifically, we formulate the optimization problem as a non-convex quadratically constrained quadratic programming (QCQP) problem. Then we design an end-to-end unified impression allocation framework to approximately solve the QCQP problem. Furthermore, experiments on real data from Tencent News show that our design significantly increases the overall revenue of both the publisher and advertisers, while achieving much faster convergence than the current state-of-the-art methods.

Guaranteed delivery (GD) and real-time bidding (RTB) constitute two parallel profit streams for the publisher. The diverse advertiser demands (brand or instant effect) result in different selling (in bulk or via auction) and pricing (fixed unit price or various bids) patterns, which naturally raises the fusion allocation issue of breaking the two markets' barrier and selling out at the global highest price boosting the total revenue. The fusion process complicates the competition between GD and RTB, and GD contracts with overlapping targeting. The non-stationary user traffic and bid landscape further worsen the situation, making the assignment unsupervised and hard to evaluate. Thus, a static policy or coarse-grained modeling from existing work is inferior to facing the above challenges.

Factorization Machine (FM) is a widely used supervised learning approach by effectively modeling of feature interactions. Despite the successful application of FM and its many deep learning variants, treating every feature interaction fairly may degrade the performance. For example, the interactions of a useless feature may introduce noises; the importance of a feature may also differ when interacting with different features. In this work, we propose a novel model named Interaction-aware Factorization Machine (IFM) by introducing Interaction-Aware Mechanism (IAM), which comprises the feature aspect and the field aspect, to learn flexible interactions on two levels. The feature aspect learns feature interaction importance via an attention network while the field aspect learns the feature interaction effect as a parametric similarity of the feature interaction vector and the corresponding field interaction prototype. IFM introduces more structured control and learns feature interaction importance in a stratified manner, which allows for more leverage in tweaking the interactions on both feature-wise and field-wise levels. Besides, we give a more generalized architecture and propose Interaction-aware Neural Network (INN) and DeepIFM to capture higher-order interactions. To further improve both the performance and efficiency of IFM, a sampling scheme is developed to select interactions based on the field aspect importance. The experimental results from two well-known datasets show the superiority of the proposed models over the state-of-the-art methods.

Short playback lag is preferred in many urgent and interactive scenarios such as live sports and distance education. However, measurement studies have shown that many popular P2P live streaming systems still suffer from long playback lag, say, more than 100 seconds, which makes the live streaming le

P2P live streaming systems suffer a lot from long playback lag in lag-sensitive scenarios. In this paper, we propose a new approach to reducing the playback lag in P2P live streaming systems. According to measurement studies, there exist a certain amount of active peers, who stay longer and contribute more bandwidth than other peers. Inspired by this, we propose a tiered overlay design, in which peers are organized into three tiers based on their degrees of activity. We develop a set of algorithms to evaluate the peers' degrees of activity. Specifically, the backbone of the overlay consists of the peers with high activity in tier-1. These active peers are responsible for diffusing the newly generated fresh chunks to peers located in all the involved Autonomous Systems (ASes). They contribute more bandwidth and thus enjoy shorter playback lag. Further more, adaptive biased neighbor selection algorithm is employed among non-backbone peers to keep traffic locality. Evaluated by extensive simulations, the proposed algorithms can reduce the average playback lag and cross-ISP traffic greatly.