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20222026
most citedMaking Users Indistinguishable: Attribute-wise Unlearning in Recommender Systems

26 citations · 32 across the 10 of their papers we have counts for

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5 papers · 1 filter

cs.IR2026

PRISM: Personalized Recommendation via Information Synergy Module

Xinyi Zhang, Yutong Li, Peijie Sun +2

Multimodal sequential recommendation (MSR) leverages diverse item modalities to improve recommendation accuracy, while achieving effective and adaptive fusion remains challenging.…

cs.IR2025

Reproducibility Companion Paper:In-processing User Constrained Dominant Sets for User-Oriented Fairness in Recommender Systems

Yixiu Liu, Zehui He, Yuyuan Li +3

In this paper, we reproduce experimental results presented in our earlier work titled "In-processing User Constrained Dominant Sets for User-Oriented Fairness in Recommender System…

cs.IR2025

Reproducibility Companion Paper: Making Users Indistinguishable: Attribute-wise Unlearning in Recommender Systems

Yuyuan Li, Junjie Fang, Chaochao Chen +3

In this paper, we reproduce the experimental results presented in our previous work titled "Making Users Indistinguishable: Attribute-wise Unlearning in Recommender Systems," which…

cs.IR2023★ 5 cited

In-processing User Constrained Dominant Sets for User-Oriented Fairness in Recommender Systems

Zhongxuan Han, Chaochao Chen, Xiaolin Zheng +4

Recommender systems are typically biased toward a small group of users, leading to severe unfairness in recommendation performance, i.e., User-Oriented Fairness (UOF) issue. The ex…

cs.IR2022★ 1 cited

Heterogeneous Information Crossing on Graphs for Session-based Recommender Systems

Xiaolin Zheng, Rui Wu, Zhongxuan Han +3

Recommender systems are fundamental information filtering techniques to recommend content or items that meet users' personalities and potential needs. As a crucial solution to addr…