计算机科学
反事实思维
偏爱
用户建模
推论
虚假关系
用户体验设计
情报检索
人机交互
用户界面
人工智能
机器学习
哲学
认识论
经济
微观经济学
操作系统
作者
Shuaiyang Li,Dan Guo,Kang Liu,Richang Hong,Feng Xue
标识
DOI:10.1145/3539618.3591739
摘要
Multimedia-based recommendation (MMRec) utilizes multimodal content (images, textual descriptions, etc.) as auxiliary information on historical interactions to determine user preferences. Most MMRec approaches predict user interests by exploiting a large amount of multimodal contents of user-interacted items, ignoring the potential effect of multimodal content of user-uninteracted items. As a matter of fact, there is a small portion of user preference-irrelevant features in the multimodal content of user-interacted items, which may be a kind of spurious correlation with user preferences, thereby degrading the recommendation performance. In this work, we argue that the multimodal content of user-uninteracted items can be further exploited to identify and eliminate the user preference-irrelevant portion inside user-interacted multimodal content, for example by counterfactual inference of causal theory. Going beyond multimodal user preference modeling only using interacted items, we propose a novel model called Multimodal Counterfactual Learning Network (MCLN), in which user-uninteracted items' multimodal content is additionally exploited to further purify the representation of user preference-relevant multimodal content that better matches the user's interests, yielding state-of-the-art performance. Extensive experiments are conducted to validate the effectiveness and rationality of MCLN. We release the complete codes of MCLN at https://github.com/hfutmars/MCLN.
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