Preference transfer model in collaborative filtering for implicit data

作     者：Bin JU Yun-tao QIAN Min-chao YE 

作者机构：Institute of Artificial IntelligenceCollege of Computer Science and TechnologyZhejiang UniversityHangzhou 3100Z7China Health Information Center of Zhejiang ProvinceHangzhou 310006China 

出 版 物：《Frontiers of Information Technology & Electronic Engineering》 (信息与电子工程前沿（英文版）)

年 卷 期：2016年第17卷第6期

页      面：489-500页

核心收录：

学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Basic Research Program(973)of China(No.2012CB316400) the National Natural Science Foundation of China(No.61571393) 

主　　题：Recommender systems Collaborative filtering Preference transfer model Cross domain Implicit data 

摘      要：Generally, predicting whether an item will be liked or disliked by active users, and how much an item will be liked, is a main task of collaborative filtering systems or recommender systems. Recently, predicting most likely bought items for a target user, which is a subproblem of the rank problem of collaborative filtering, became an important task in collaborative filtering. Traditionally, the prediction uses the user item co-occurrence data based on users buying behaviors. However, it is challenging to achieve good prediction performance using traditional methods based on single domain information due to the extreme sparsity of the buying matrix. In this paper, we propose a novel method called the preference transfer model for effective cross-domain collaborative filtering. Based on the preference transfer model, a common basis item-factor matrix and different user-factor matrices are *** user-factor matrix can be viewed as user preference in terms of browsing behavior or buying behavior. Then,two factor-user matrices can be used to construct a so-called ‘preference dictionary that can discover in advance the consistent preference of users, from their browsing behaviors to their buying behaviors. Experimental results demonstrate that the proposed preference transfer model outperforms the other methods on the Alibaba Tmall data set provided by the Alibaba Group.