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• W3204126110 abstract "Recently, textual information has been proved to play a positive role inrecommendation systems. However, most of the existing methods only focus onrepresentation learning of textual information in ratings, while potentialselection bias induced by the textual information is ignored. In this work, wepropose a novel and general self-adaptive module, the Self-adaptive AttentionModule (SAM), which adjusts the selection bias by capturing contextualinformation based on its representation. This module can be embedded intorecommendation systems that contain learning components of contextualinformation. Experimental results on three real-world datasets demonstrate theeffectiveness of our proposal, and the state-of-the-art models with SAMsignificantly outperform the original ones." @default.
• W3204126110 created "2021-10-11" @default.
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• W3204126110 date "2021-10-01" @default.
• W3204126110 modified "2023-09-23" @default.
• W3204126110 title "SAM: A Self-adaptive Attention Module for Context-Aware Recommendation System." @default.
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