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Personalized Semantic Ranking for Collaborative Recommendation
Xu, Song; Wu, Shu; Wang, Liang
2015
会议名称ACM SIGIR Conference on Research and Development in Information Retrieval
会议录名称In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2015
会议日期August 9-13
会议地点Santiago
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
关键词Learning To Rank Recommendation User-generated Content
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/12340
专题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],2015.
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