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Bilateral Self-unbiased Learning from Biased Implicit Feedback
[article]
2022
arXiv
pre-print
Implicit feedback has been widely used to build commercial recommender systems. Because observed feedback represents users' click logs, there is a semantic gap between true relevance and observed feedback. More importantly, observed feedback is usually biased towards popular items, thereby overestimating the actual relevance of popular items. Although existing studies have developed unbiased learning methods using inverse propensity weighting (IPW) or causal reasoning, they solely focus on
arXiv:2207.12660v1
fatcat:lsxnmawzdrfzri3eooig3ikr7e