86 citations · 289 across the 34 of their papers we have counts for
6 papers · 1 filter
CCL4Rec: Contrast over Contrastive Learning for Micro-video Recommendation
Shengyu Zhang, Bofang Li, Dong Yao +7
Micro-video recommender systems suffer from the ubiquitous noises in users' behaviors, which might render the learned user representation indiscriminating, and lead to trivial reco…
BARS: Towards Open Benchmarking for Recommender Systems
Jieming Zhu, Quanyu Dai, Liangcai Su +5
The past two decades have witnessed the rapid development of personalized recommendation techniques. Despite significant progress made in both research and practice of recommender…
ReLoop: A Self-Correction Continual Learning Loop for Recommender Systems
Guohao Cai, Jieming Zhu, Quanyu Dai +4
Deep learning-based recommendation has become a widely adopted technique in various online applications. Typically, a deployed model undergoes frequent re-training to capture users…
Multi-Level Interaction Reranking with User Behavior History
Yunjia Xi, Weiwen Liu, Jieming Zhu +6
As the final stage of the multi-stage recommender system (MRS), reranking directly affects users' experience and satisfaction, thus playing a critical role in MRS. Despite the impr…
Contrastive Learning with Positive-Negative Frame Mask for Music Representation
Dong Yao, Zhou Zhao, Shengyu Zhang +4
Self-supervised learning, especially contrastive learning, has made an outstanding contribution to the development of many deep learning research fields. Recently, researchers in t…
PEAR: Personalized Re-ranking with Contextualized Transformer for Recommendation
Yi Li, Jieming Zhu, Weiwen Liu +6
The goal of recommender systems is to provide ordered item lists to users that best match their interests. As a critical task in the recommendation pipeline, re-ranking has receive…