Personalized Semantic Ranking for Collaborative Recommendation

CASIA OpenIR > 模式识别实验室

	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	模式识别实验室
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.

条目包含的文件		下载所有文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
Personalized Semanti（969KB）	会议论文		开放获取	CC BY-NC-SA	浏览下载