WSDM2022推荐系统论文集锦

第15届国际网络搜索与数据挖掘会议wsdm将于2022年2月21日至25日通过线上方式举行。本届会议共收到786份有效投稿，最终录取了159篇论文，录取率为20.23%。历年论文投稿量和录取率可在下图中查看。

作为主流的搜索与数据挖掘会议，WSDM的论文主要聚焦于搜索、推荐和数据挖掘领域。因此，大部分接收的论文主题围绕信息检索与推荐系统展开。如需了解去年及前年的WSDM相关信息，可参考：

WSDM2021推荐系统论文集锦 WSDM2020推荐系统论文清单

本次会议将举办与信息检索和推荐系统相关的教程，其中特别值得关注的是基于图神经网络的推荐系统教程。以下是教程大纲：

推荐系统相关文章

从159篇论文中，我们精选了与推荐系统紧密相关的34篇文章，涵盖了序列推荐、跨域推荐、点击率预估、在线推荐、去偏推荐、联邦推荐、对话推荐、知识图谱推荐、组推荐、会话推荐、可解释性推荐以及路线推荐等主题。

从技术角度来看，这些论文主要采用了在线学习、元学习、强化学习、对抗训练、图神经网络、对比学习、随机游走、迁移学习等方法。

以下列出了相关论文，供大家提前了解学术前沿趋势和顶尖研究者的最新想法。

跨域推荐

RecGURU: Adversarial Learning of Generalized User Representations for Cross-Domain Recommendation

https://www.php.cn/link/e5cebc6f1325b090a06621c52e0f4e91

Personalized Transfer of User Preferences for Cross-domain Recommendation

https://www.php.cn/link/c44953f2f780be79d8f60e568c9bd1e4

Multi-Sparse-Domain Collaborative Recommendation via Enhanced Comprehensive Aspect Preference Learning

序列推荐

Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation

https://www.php.cn/link/a7a0da96b4c16537050114ceed8368c9

S-Walk: Accurate and Scalable Session-based Recommendation with Random Walks

Heterogeneous Global Graph Neural Networks for Personalized Session-based Recommendation

https://www.php.cn/link/2e405b15462930fe38bd7ab3ca37869b

Learning Multi-granularity Consecutive User Intent Unit for Session-based Recommendation

点击率预估

CAN: Feature Co-Action Network for Click-Through Rate Prediction

Triangle Graph Interest Network for Click-through Rate Prediction

Modeling Users’ Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce Search

去偏推荐

It Is Different When Items Are Older: Debiasing Recommendations When Selection Bias and User Preferences are Dynamic

https://www.php.cn/link/26f6abfa0d7725fef678e371897d5df0

Fighting Mainstream Bias in Recommender Systems via Local Fine Tuning

https://www.php.cn/link/2300d8171ca46be1ee5a72d2837c1b6d

Towards Unbiased and Robust Causal Ranking for Recommender Systems

路径推荐

PLdFe-RR:Personalized Long-distance Fuel-efficient Route Recommendation Based On Historical Trajectory

联邦推荐

PipAttack: Poisoning Federated Recommender Systems for Manipulating Item Promotion

https://www.php.cn/link/1b04e3e4deb99316836eb317f939347a

基于图结构的推荐

Joint Learning of E-commerce Search and Recommendation with A Unified Graph Neural Network

文赋Ai论文

专业/高质量智能论文AI生成器-在线快速生成论文初稿

37

查看详情

Profiling the Design Space for Graph Neural Networks based Collaborative Filtering

https://www.php.cn/link/7ffc2fa46fbc98fb9794db5e1f7d4896

Graph Logic Reasoning for Recommendation and Link Prediction

Modeling Scale-free Graphs with Hyperbolic Geometry for Knowledge-aware Recommendation

https://www.php.cn/link/e1f70e23dce0d941aa028f900244a094

公平性推荐

Toward Pareto Efficient Fairness-Utility Trade-off in Recommendation through Reinforcement Learning

Enumerating Fair Packages for Group Recommendations

https://www.php.cn/link/efba0d602748d6f5066384a22b186e5b

基于对比学习的推荐

Contrastive Meta Learning with Behavior Multiplicity for Recommendation

C2-CRS: Coarse-to-Fine Contrastive Learning for Conversational Recommender System

基于元学习的推荐

Long Short-Term Temporal Meta-learning in Online Recommendation

https://www.php.cn/link/c44953f2f780be79d8f60e568c9bd1e40

基于对抗学习的推荐

A Peep into the Future: Adversarial Future Encoding in Recommendation

基于强化学习的推荐

Reinforcement Learning over Sentiment-Augmented Knowledge Graphs towards Accurate and Explainable Recommendation

A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertising

https://www.php.cn/link/c44953f2f780be79d8f60e568c9bd1e41

Choosing the Best of All Worlds: Accurate, Diverse, and Novel Recommendations through Multi-Objective Reinforcement Learning

https://www.php.cn/link/c44953f2f780be79d8f60e568c9bd1e42

关于数据集

On Sampling Collaborative Filtering Datasets

The Datasets Dilemma: How Much Do We Really Know About Recommendation Datasets?

其他

VAE++: Variational AutoEncoder for Heterogeneous One-Class Collaborative Filtering

Sequential Modeling with Multiple Attributes for Watchlist Recommendation in E-Commerce

https://www.php.cn/link/c44953f2f780be79d8f60e568c9bd1e43

Show Me the Whole World: Towards Entire Item Space Exploration for Interactive Personalized Recommendations

https://www.php.cn/link/c44953f2f780be79d8f60e568c9bd1e44

Supervised Advantage Actor-Critic for Recommender Systems

https://www.php.cn/link/c44953f2f780be79d8f60e568c9bd1e45

官方接收论文列表地址：

https://www.php.cn/link/c44953f2f780be79d8f60e568c9bd1e46

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