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
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：
