13 citations · 13 across the 3 of their papers we have counts for
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cs.IR2026
Probabilistic Residual Learning for Online Recommendations
Wenyuan Wang, Yusong Zhao, Zihao Xu +11
Modern recommender systems are typically based on deep learning (DL) models, where a dense encoder learns representations of users and items. As a result, these systems often suffe…
cs.IR2023
An Incremental Update Framework for Online Recommenders with Data-Driven Prior
Chen Yang, Jin Chen, Qian Yu +10
Online recommenders have attained growing interest and created great revenue for businesses. Given numerous users and items, incremental update becomes a mainstream paradigm for le…
cs.IR2021★ 13 cited
Popularity Bias Is Not Always Evil: Disentangling Benign and Harmful Bias for Recommendation
Zihao Zhao, Jiawei Chen, Sheng Zhou +4
Recommender system usually suffers from severe popularity bias -- the collected interaction data usually exhibits quite imbalanced or even long-tailed distribution over items. Such…