63 citations · 86 across the 6 of their papers we have counts for
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cs.IR2023
Measuring Item Global Residual Value for Fair Recommendation
Jiayin Wang, Weizhi Ma, Chumeng Jiang +4
In the era of information explosion, numerous items emerge every day, especially in feed scenarios. Due to the limited system display slots and user browsing attention, various rec…
cs.IR2023★ 63 cited
Alleviating Matthew Effect of Offline Reinforcement Learning in Interactive Recommendation
Chongming Gao, Kexin Huang, Jiawei Chen +6
Offline reinforcement learning (RL), a technology that offline learns a policy from logged data without the need to interact with online environments, has become a favorable choice…
cs.IR2023
Divide and Conquer: Towards Better Embedding-based Retrieval for Recommender Systems From a Multi-task Perspective
Yuan Zhang, Xue Dong, Weijie Ding +3
Embedding-based retrieval (EBR) methods are widely used in modern recommender systems thanks to its simplicity and effectiveness. However, along the journey of deploying and iterat…