Learning to Hash for Recommendation with Tensor Data

CASIA OpenIR > 模式识别实验室

	Learning to Hash for Recommendation with Tensor Data
	Yin, Qiyue; Wu, Shu; Wang, Liang
	2015
会议名称	Proc. Asia Pacific Web Conference
页码	292-303
会议日期	2015-09-18
会议地点	Guangzhou, China
摘要	Recommender systems usually need to compare user interests and item characteristics in the context of large user and item space, making hashing based algorithms a promising strategy to speed up recommendation. Existing hashing based recommendation methods only model the users and items and dealing with the matrix data, e.g., user-item rating matrix. In practice, recommendation scenarios can be rather complex, e.g., collaborative retrieval and personalized tag recommendation. The above scenarios generally need fast search for one type of entities (target entities) using multiple types of entities (source entities). The resulting three or higher order tensor data makes conventional hashing algorithms fail for the above scenarios. In this paper, a novel hashing method is accordingly proposed to solve the above problem, where the tensor data is approached by properly designing the similarities between the source entities and target entities in Hamming space. Besides, operator matrices are further developed to explore the relationship between different types of source entities, resulting in auxiliary codes for source entities. Extensive experiments on two tasks, i.e., personalized tag recommendation and collaborative retrieval, demonstrate that the proposed method performs well for tensor data.
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/14065
专题	模式识别实验室
作者单位	中国科学院自动化研究所
第一作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Yin, Qiyue,Wu, Shu,Wang, Liang. Learning to Hash for Recommendation with Tensor Data[C],2015:292-303.

条目包含的文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
Learning to Hash for（355KB）	会议论文		开放获取	CC BY-NC-SA	浏览