Recommender systems help people find relevant content in a personalized way. One main
promise of such systems is that they are able to increase the visibility of items in the long tail …

Contrastive Learning (CL) has achieved impressive performance in self-supervised learning
tasks, showing superior generalization ability. Inspired by the success, adopting CL into …

Planning for both immediate and long-term benefits becomes increasingly important in
recommendation. Existing methods apply Reinforcement Learning (RL) to learn planning …

Predicting Click-Through Rate (CTR) in billion-scale recommender systems poses a long-
standing challenge for Graph Neural Networks (GNNs) due to the overwhelming …

群组推荐(Group recommendation) 在信息检索与数据挖掘领域近年来备受关注,
其旨在从海量候选集中挑选出一组用户可能感兴趣的项目. 随着深度学习技术的不断发展 …

Collaborative Filtering~(CF) typically suffers from the significant challenge of popularity bias
due to the uneven distribution of items in real-world datasets. This bias leads to a significant …

In recommendation systems (RS), user behavior data is observational rather than
experimental, resulting in widespread bias in the data. Consequently, tackling bias has …

Fairness of recommender systems (RS) has attracted increasing attention recently. Based
on the involved stakeholders, the fairness of RS can be divided into user fairness, item …

Recent studies empirically indicate that language models (LMs) encode rich world
knowledge beyond mere semantics, attracting significant attention across various fields …

Recommendation Systems (RS) are often plagued by popularity bias. When training a
recommendation model on a typically long-tailed dataset, the model tends to not only inherit …