Fast Tensor Factorization for Large-Scale Context-Aware Recommendation from Implicit Feedback

作     者：Chou, Szu-Yu Jang, Jyh-Shing Roger Yang, Yi-Hsuan 

作者机构：Acad Sinica Res Ctr IT Innovat Taipei 11529 Taiwan Natl Taiwan Univ Grad Inst Networking & Multimedia Taipei 10617 Taiwan 

出 版 物：《IEEE TRANSACTIONS ON BIG DATA》 (IEEE Trans. Big Data)

年 卷 期：2020年第6卷第1期

页      面：201-208页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Ministry of Science and Technology of Taiwan [MOST 106-3114-E-002-007] 

主　　题：Recommender system collaborative filtering context-aware recommendation tensor factorization implicit feedback 

摘      要：This paper presents a fast Tensor Factorization (TF) algorithm for context-aware recommendation from implicit feedback. For such a recommendation problem, the observed data indicate the (positive) association between users and items in some given contexts. For better accuracy, it has been shown essential to include unobserved data that indicate the negative user-item-context associations. As such unobserved data greatly outnumber the observed ones, for efficiency existing algorithms usually use only a small part of the unobserved data for model training. We show in this paper that it is possible, and beneficial, to use all the unobserved data in training a TF based context-aware recommender system. This is achieved by two technical innovations. First, we scrutinize the matrix computation of the closed-form solution and accelerate the computation by memorizing the repetitive computation. Second, we further boost the generalization and scalability by dropping out some pairwise interactions when updating user, item or context factors to prevent overfitting and to reduce the training time. The resulting whole-data based learning algorithm, referred to as DropTF in the paper, is efficient and scale well. Our evaluation on two small benchmark datasets and a million-scale large dataset demonstrates improved accuracy over some existing algorithms for context-aware recommendation.