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• W4284689794 abstract "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 eliminating the popularity bias of items. In this paper, we propose a novel unbiased recommender learning model, namely BIlateral SElf-unbiased Recommender (BISER), to eliminate the exposure bias of items caused by recommender models. Specifically, BISER consists of two key components: (i) self-inverse propensity weighting (SIPW) to gradually mitigate the bias of items without incurring high computational costs; and (ii) bilateral unbiased learning (BU) to bridge the gap between two complementary models in model predictions, i.e., user- and item-based autoencoders, alleviating the high variance of SIPW. Extensive experiments show that BISER consistently outperforms state-of-the-art unbiased recommender models over several datasets, including Coat, Yahoo! R3, MovieLens, and CiteULike." @default.
• W4284689794 created "2022-07-08" @default.
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• W4284689794 date "2022-07-06" @default.
• W4284689794 modified "2023-09-29" @default.
• W4284689794 title "Bilateral Self-unbiased Learning from Biased Implicit Feedback" @default.
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• W4284689794 doi "https://doi.org/10.1145/3477495.3531946" @default.
• W4284689794 hasPublicationYear "2022" @default.
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