DeepStyle: Learning User Preferences for Visual Recommendation
文献类型：会议论文
| 作者 | Liu, Qiang1,2 ; Wu, Shu1,2; Wang, Liang1,2
|
| 出版日期 | 2017-08 |
| 会议日期 | 2017-8 |
| 会议地点 | Tokyo, Japan |
| 关键词 | Visual Recommendation User Preferences Style Features |
| 英文摘要 | 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. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/19617]  |
| 专题 | 自动化研究所_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]. 见:. Tokyo, Japan. 2017-8. |
入库方式： OAI收割
来源：自动化研究所
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