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:
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:
跨域推荐
https://arxiv.org/pdf/2111.10093.pdf
https://arxiv.org/pdf/2110.11154.pdf
Multi-Sparse-Domain Collaborative Recommendation via Enhanced Comprehensive Aspect Preference Learning
序列推荐
S-Walk: Accurate and Scalable Session-based Recommendation with Random Walks
https://arxiv.org/pdf/2107.03813.pdf
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
路径推荐
PLdFe-RR:Personalized Long-distance Fuel-efficient Route Recommendation Based On Historical Trajectory
联邦推荐
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
http://www.shichuan.org/doc/125.pdf
Graph Logic Reasoning for Recommendation and Link Prediction
https://arxiv.org/pdf/2108.06468.pdf
公平性推荐
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
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
A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertising
https://arxiv.org/pdf/2106.06224.pdf
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
Sequential Modeling with Multiple Attributes for Watchlist Recommendation in E-Commerce
Show Me the Whole World: Towards Entire Item Space Exploration for Interactive Personalized Recommendations
Supervised Advantage Actor-Critic for Recommender Systems