5 citations · 15 across the 20 of their papers we have counts for
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cs.IR2023★ 1 cited
Unveiling Vulnerabilities of Contrastive Recommender Systems to Poisoning Attacks
Zongwei Wang, Junliang Yu, Min Gao +3
Contrastive learning (CL) has recently gained prominence in the domain of recommender systems due to its great ability to enhance recommendation accuracy and improve model robustne…
cs.LG2023★ 2 cited
Debiasing Recommendation by Learning Identifiable Latent Confounders
Qing Zhang, Xiaoying Zhang, Yang Liu +4
Recommendation systems aim to predict users' feedback on items not exposed to them. Confounding bias arises due to the presence of unmeasured variables (e.g., the socio-economic st…