Personalized Semantic Ranking for Collaborative Recommendation
文献类型：会议论文
| 作者 | Xu, Song; Wu, Shu ; Wang, Liang
|
| 出版日期 | 2015 |
| 会议日期 | August 9-13 |
| 会议地点 | Santiago |
| 关键词 | Learning To Rank Recommendation User-generated Content |
| 英文摘要 | Recently a ranking view of collaborative recommendation has received much attention in recommendation systems. Most of existing ranking approaches are based on pairwise assumption, i.e., everything that has not been selected is of less interest for a user. However it is usually not proper in many cases. To alleviate the limitation of this assumption, in this work, we present a unified framework, named Personalized Semantic Ranking (PSR). PSR models the personalized ranking and the user-generated content (UGC) simultaneously, and the semantic information extracted from UGC can make a remedy for the pairwise assumption. Moreover, utilizing the semantic information, PSR can capture the more subtle information of the user-item interaction and alleviate the overfitting problem caused by insufficient ratings. The learned topics in PSR can also serve as proper explanations for recommendation. Experimental results show that the proposed PSR yields significant improvements over the competitive compared methods on two typical datasets |
| 会议录 | In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2015
 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/12340]  |
| 专题 | 自动化研究所_智能感知与计算研究中心 |
| 通讯作者 | Wu, Shu |
| 推荐引用方式 GB/T 7714 | Xu, Song,Wu, Shu,Wang, Liang. Personalized Semantic Ranking for Collaborative Recommendation[C]. 见:. Santiago. August 9-13. |
入库方式： OAI收割
来源：自动化研究所
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