An E-Commerce Recommender System Based on Click and Purchase Data to Items and Considered of Interest Shifting of Customers

作     者：Duo Lin Wu Zhaoxia XU Shenggang 

作者机构：School of Computer and Communication Engineering University of Science and Technology Beijing Faculty of Information Engineering and Automation Kunming University of Science and Technology School of Applied Mathematics Xinjiang University of Finance and Economics 

出 版 物：《China Communications》 (中国通信（英文版）)

年 卷 期：2015年第12卷第S2期

页      面：72-82页

核心收录：

学科分类：0810[工学-信息与通信工程] 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 theNational High-Tech R&D Program (863 Program) No. 2015AA01A705 the National Natural Science Foundation of China under Grant No. 61572072 the National Science and Technology Major Project No. 2015ZX03001041 Fundamental Research Funds for the Central Universities No. FRF-TP-15-027A3 Yunnan Provincial Department of Education Foundation Project (No. 2014Y087) 

主　　题：recommender system online shopping e-commerce preference degree 

摘      要：A well-performed recommender system for an e-commerce web site can help customers easily find favorite items and then increase the turnover of merchants, hence it is important for both customers and merchants. In most of the existing recommender systems, only the purchase information is utilized data and the navigational and behavioral data are seldom concerned. In this paper, we design a novel recommender system for comprehensive online shopping sites. In the proposed recommender system, the navigational and behavioral data, such as access, click, read, and purchase information of a customer, are utilized to calculate the preference degree to each item; then items with larger preference degrees are recommended to the customer. The proposed method has several innovations and two of them are more remarkable: one is that nonexpendable items are distinguished from expendable ones and handled by a different way; another is that the interest shifting of customers are considered. Lastly, we structure an example to show the operation procedure and the performance of the proposed recommender system. The results show that the proposed recommender method with considering interest shifting is superior to Kim et al(2011) method and the method without considering interest shifting.