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DeepStyle: Learning User Preferences for Visual Recommendation
Liu, Qiang1,2; Wu, Shu1,2; Wang, Liang1,2
Conference NameInternational Conference on Research on Development in Information Retrieval (SIGIR)
Conference Date2017-8
Conference PlaceTokyo, Japan
AbstractVisual information is an important factor in recommender systems. Some studies have been done to model user preferences for visual recommendation. Usually, an item consists of two fundamental components: style and category. Conventional methods model items in a common visual feature space. In these methods, visual representations always can only capture the categorical information but fail in capturing the styles of items. Style information indicates the preferences of users and has significant effect in visual recommendation. Accordingly, we propose a DeepStyle method for learning style features of items and sensing preferences of users. Experiments conducted on two real-world datasets illustrate the effectiveness of DeepStyle for visual recommendation.
KeywordVisual Recommendation User Preferences Style Features
Document Type会议论文
Affiliation1.Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Liu, Qiang,Wu, Shu,Wang, Liang. DeepStyle: Learning User Preferences for Visual Recommendation[C],2017.
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