COT: Contextual Operating Tensor for Context-aware Recommender Systems

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	COT: Contextual Operating Tensor for Context-aware Recommender Systems
	Liu, Qiang; Wu, Shu; Wang, Liang
	2015
会议日期	January 25-30
会议地点	Austin
关键词	Recommender Systems Context-aware Contextual Operating
英文摘要	With rapid growth of information on the internet, recommender systems become fundamental for helping users alleviate the problem of information overload. Since contextual information can be used as a signifi- cant factor in modeling user behavior, various contextaware recommendation methods are proposed. However, the state-of-the-art context modeling methods treat contexts as other dimensions similar to the dimensions of users and items, and cannot capture the special semantic operation of contexts. On the other hand, some works on multi-domain relation prediction can be used for the context-aware recommendation, but they have problems in generating recommendation under a large amount of contextual information. In this work, we propose Contextual Operating Tensor (COT) model, which represents the common semantic effects of contexts as a contextual operating tensor and represents a context as a latent vector. Then, to model the semantic operation of a context combination, we generate contextual operating matrix from the contextual operating tensor and latent vectors of contexts. Thus latent vectors of users and items can be operated by the contextual operating matrices. Experimental results show that the proposed COT model yields significant improvements over the competitive compared methods on three typical datasets, i.e., Food, Adom and Movielens-1M datasets
会议录	In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI), 2015
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/12347]
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
作者单位	Institute of Automation, Chinese Academy of Sciences
推荐引用方式 GB/T 7714	Liu, Qiang,Wu, Shu,Wang, Liang. COT: Contextual Operating Tensor for Context-aware Recommender Systems[C]. 见:. Austin. January 25-30.