ICML2023推荐系统论文整理
第四十届国际机器学习会议ICML已在7月23日到29日在夏威夷会议中心举行。今年大会共收到6,538篇投稿，最终录取1,827篇，录用率为27.94%。我们从所有接收列表中筛选出与推荐系统主题相关的论文6篇，以供大家进行阅读和学习。
大会论文接收列表地址：
https://icml.cc/virtual/2023/papers.html
所整理的推荐系统论文中主题主要包括社交推荐、联邦图神经网络推荐、多样性推荐、去偏推荐以及点击率预估等。
- 1. Curriculum Co-disentangled Representation Learning across Multiple Environments for Social Recommendation
- 2. Vertical Federated Graph Neural Network for Recommender System
- 3. How Bad is Top-K Recommendation under Competing Content Creators?
- 4. Performative Recommendation: Diversifying Content via Strategic Incentives
- 5. Propensity Matters: Measuring and Enhancing Balancing for Recommendation
- 6. Improved Online Learning Algorithms for CTR Prediction in Ad Auctions
1. Curriculum Co-disentangled Representation Learning across Multiple Environments for Social Recommendation
Xin Wang, Zirui Pan, Yuwei Zhou, Hong Chen, Chendi Ge, Wenwu Zhu
https://proceedings.mlr.press/v202/wang23z/wang23z.pdf
There exist complex patterns behind the decision-making processes of different individuals across different environments. For instance, in a social recommender system, various user behaviors are driven by highly entangled latent factors from two environments, i.e., consuming environment where users consume items and social environment where users connect with each other. Uncovering the disentanglement of these latent factors for users can benefit in enhanced explainability and controllability for recommendation. However, in literature there has been no work on social recommendation capable of disentangling user representations across consuming and social environments. To solve this problem, we study co-disentangled representation learning across different environments via proposing the curriculum co-disentangled representation learning (CurCoDis) model to disentangle the hidden factors for users across both consuming and social environments. To co-disentangle joint representations for user-item consumption and user-user social graph simultaneously, we partition the social graph into equal-size sub-graphs with minimum number of edges being cut, and design a curriculum weighing strategy for subgraph training through measuring the complexity of subgraphs via Descartes’ rule of signs. We further develop the prototype-routing optimization mechanism, which achieves co-disentanglement of user representations across consuming and social environments. Extensive experiments for social recommendation demonstrate that our proposed CurCoDis model can significantly outperform state-of-the-art methods on several real-world datasets.
2. Vertical Federated Graph Neural Network for Recommender System
Peihua Mai, Yan Pang
https://proceedings.mlr.press/v202/mai23b/mai23b.pdf
Conventional recommender systems are required to train the recommendation model using a centralized database. However, due to data privacy concerns, this is often impractical when multi-parties are involved in recommender system training. Federated learning appears as an excellent solution to the data isolation and privacy problem. Recently, Graph neural network (GNN) is becoming a promising approach for federated recommender systems. However, a key challenge is to conduct embedding propagation while preserving the privacy of the graph structure. Few studies have been conducted on the federated GNN-based recommender system. Our study proposes the first vertical federated GNN-based recommender system, called VerFedGNN. We design a framework to transmit: (i) the summation of neighbor embeddings using random projection, and (ii) gradients of public parameter perturbed by ternary quantization mechanism. Empirical studies show that VerFedGNN has competitive prediction accuracy with existing privacy preserving GNN frameworks while enhanced privacy protection for users’ interaction information.
3. How Bad is Top-K Recommendation under Competing Content Creators?
Fan Yao, Chuanhao Li, Denis Nekipelov, Hongning Wang, Haifeng Xu
https://proceedings.mlr.press/v202/yao23b/yao23b.pdf
This study explores the impact of content creators’ competition on user welfare in recommendation platforms, as well as the long-term dynamics of relevance-driven recommendations. We establish a model of creator competition, under the setting where the platform uses a top-K recommendation policy, user decisions are guided by the Random Utility model, and creators, in absence of explicit utility functions, employ arbitrary no-regret learning algorithms for strategy updates. We study the user welfare guarantee through the lens of Price of Anarchy and show that the fraction of user welfare loss due to creator competition is always upper bounded by a small constant depending on K and randomness in user decisions; we also prove the tightness of this bound. Our result discloses an intrinsic merit of the relevance-driven recommendation policy, as long as users’ decisions involve randomness and the platform provides reasonably many alternatives to its users.
4. Performative Recommendation: Diversifying Content via Strategic Incentives
Itay Eilat, Nir Rosenfeld
https://proceedings.mlr.press/v202/eilat23a/eilat23a.pdf
The primary goal in recommendation is to suggest relevant content to users, but optimizing for accuracy often results in recommendations that lack diversity. To remedy this, conventional approaches such as re-ranking improve diversity by presenting more diverse items. Here we argue that to promote inherent and prolonged diversity, the system must encourage its creation. Towards this, we harness the performative nature of recommendation, and show how learning can incentivize strategic content creators to create diverse content. Our approach relies on a novel form of regularization that anticipates strategic changes to content, and penalizes for content homogeneity. We provide analytic and empirical results that demonstrate when and how diversity can be incentivized, and experimentally demonstrate the utility of our approach on synthetic and semi-synthetic data.
5. Propensity Matters: Measuring and Enhancing Balancing for Recommendation
Haoxuan Li, Yanghao Xiao, Chunyuan Zheng, Peng Wu, Peng Cui
https://proceedings.mlr.press/v202/li23ah/li23ah.pdf
Propensity-based weighting methods have been widely studied and demonstrated competitive performance in debiased recommendations. Nevertheless, there are still many questions to be addressed. How to estimate the propensity more conducive to debiasing performance? Which metric is more reasonable to measure the quality of the learned propensities? Is it better to make the cross-entropy loss as small as possible when learning propensities? In this paper, we first discuss the potential problems of the previously widely adopted metrics for learned propensities, and propose balanced-mean-squared-error (BMSE) metric for debiased recommendations. Based on BMSE, we propose IPS-V2 and DR-V2 as the estimators of unbiased loss, and theoretically show that IPS-V2 and DR-V2 have greater propensity balancing and smaller variance without sacrificing additional bias. We further propose a co-training method for learning balanced representation and unbiased prediction. Extensive experiments are conducted on three real-world datasets including a large industrial dataset, and the results show that our approach boosts the balancing property and results in enhanced debiasing performance.
6. Improved Online Learning Algorithms for CTR Prediction in Ad Auctions
Zhe Feng, Christopher Liaw, Zixin Zhou
https://proceedings.mlr.press/v202/feng23b/feng23b.pdf
In this work, we investigate the online learning problem of revenue maximization in ad auctions, where the seller needs to learn the click-through rates (CTRs) of each ad candidate and charge the price of the winner through a pay-per-click manner. We focus on two models of the advertisers’ strategic behaviors. First, we assume that the advertiser is completely myopic; i.e. in each round, they aim to maximize their utility only for the current round. In this setting, we develop an online mechanism based on upper-confidence bounds that achieves a tight regret in the worst-case and negative regret when the values are static across all the auctions and there is a gap between the highest expected value (i.e. value multiplied by their CTR) and second highest expected value ad. Next, we assume that the advertiser is non-myopic and cares about their long term utility. This setting is much more complex since an advertiser is incentivized to influence the mechanism by bidding strategically in earlier rounds. In this setting, we provide an algorithm to achieve negative regret for the static valuation setting (with a positive gap), which is in sharp contrast with the prior work that shows regret when the valuation is generated by adversary.