2022年第15届国际网络搜索与数据挖掘会议WSDM将在2022年2月21日到25日于线上举行。今年此次会议共收到了786份有效投稿，最终录取篇数为159篇，录取率为20.23%。该会议历年的论文投稿量以及接收率可见下图。
作为主流的搜索与数据挖掘会议，论文的话题主要侧重于搜索、推荐以及数据挖掘领域，因此该会议大部分的接收论文的主题是围绕着信息检索与推荐系统来说的。若想了解去年以及前年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
该会议将举办一些围绕信息检索、推荐系统相关的教程，其中可以重点关注下基于图神经网络的推荐系统教程，以下为教程的大纲：
推荐系统相关文章
接下来，特意从159篇论文中筛选出与推荐系统强相关的34篇文章供大家欣赏，其中从主题上看大致包括了序列推荐、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。
从所使用的技术上划分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。
下文将列出相关的论文，供大家提前领略学术前沿趋势与牛人的最新想法。
跨域推荐
RecGURU: Adversarial Learning of Generalized User Representations for Cross-Domain Recommendation
https://arxiv.org/pdf/2111.10093.pdf
Personalized Transfer of User Preferences for Cross-domain Recommendation
https://arxiv.org/pdf/2110.11154.pdf
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://arxiv.org/pdf/2110.05730.pdf
S-Walk: Accurate and Scalable Session-based Recommendation with Random Walks
Heterogeneous Global Graph Neural Networks for Personalized Session-based Recommendation
https://arxiv.org/pdf/2107.03813.pdf
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://arxiv.org/pdf/2111.12481.pdf
Fighting Mainstream Bias in Recommender Systems via Local Fine Tuning
http://people.tamu.edu/~zhuziwei/pubs/Ziwei_WSDM_2022.pdf
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://arxiv.org/pdf/2110.10926.pdf
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
http://www.shichuan.org/doc/125.pdf
Graph Logic Reasoning for Recommendation and Link Prediction
Modeling Scale-free Graphs with Hyperbolic Geometry for Knowledge-aware Recommendation
https://arxiv.org/pdf/2108.06468.pdf
公平性推荐
Toward Pareto Efficient Fairness-Utility Trade-off in Recommendation through Reinforcement Learning
Enumerating Fair Packages for Group Recommendations
https://arxiv.org/pdf/2105.14423.pdf
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://arxiv.org/pdf/2105.03686.pdf
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://arxiv.org/pdf/2106.06224.pdf
Choosing the Best of All Worlds: Accurate, Diverse, and Novel Recommendations through Multi-Objective Reinforcement Learning
https://arxiv.org/pdf/2110.15097.pdf
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://arxiv.org/pdf/2110.11072.pdf
Show Me the Whole World: Towards Entire Item Space Exploration for Interactive Personalized Recommendations
https://arxiv.org/pdf/2110.09905.pdf
Supervised Advantage Actor-Critic for Recommender Systems
https://arxiv.org/pdf/2111.03474.pdf
官网接收论文列表地址：
https://www.wsdm-conference.org/2022/accepted-papers/
