嘿，记得给” 机器学习与推荐算法 “添加 星标
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：
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：
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Original: https://blog.csdn.net/weixin_44289754/article/details/122593212
Author: 机器学习与推荐算法
Title: WSDM2022推荐系统论文集锦
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