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DeepStyle: Learning User Preferences for Visual Recommendation
Liu, Qiang1,2; Wu, Shu1,2; Wang, Liang1,2
2017-08
会议名称International Conference on Research on Development in Information Retrieval (SIGIR)
会议日期2017-8
会议地点Tokyo, Japan
摘要Visual 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.
关键词Visual Recommendation User Preferences Style Features
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/19617
专题智能感知与计算研究中心
作者单位1.Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
推荐引用方式
GB/T 7714
Liu, Qiang,Wu, Shu,Wang, Liang. DeepStyle: Learning User Preferences for Visual Recommendation[C],2017.
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