A Behavior Mining Based Hybrid Recommender System

Fang, Zhiyuan; Zhang, Lingqi; Chen, Kun

10.1109/ICBDA.2016.7509785

2016

PROCEEDINGS OF 2016 IEEE INTERNATIONAL CONFERENCE ON BIG DATA ANALYSIS (ICBDA)

1-5

Hangzhou, China

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

Recommender systems are mostly well known for their applications in e-commerce sites and are mostly static models. Classical personalized recommender algorithm include collaborative filtering method applied in Amazon, matrix factorization algorithm from Netflix,etc. In this article, we hope to combine traditional model with behavior pattern extraction method. We use desensitized mobile transaction record provided by T-mall, Alibaba to build a hybrid dynamic recommender system. The sequential pattern mining aims to find frequent sequential pattern in sequence database and is applied in this hybrid model to predict customers' payment behavior thus contributing to the accuracy of the model.

recommender system behavior minin T-mall hybrid model

第一 ; 通讯

英语

CPCI-S ; EI

Computer Science ; Engineering

Computer Science, Information Systems ; Engineering, Electrical & Electronic

WOS:000390299100001

20163302717733

Big data ; Collaborative filtering ; Data handling ; Electronic commerce ; Factorization

Data Processing and Image Processing:723.2 ; Computer Applications:723.5 ; Information Sources and Analysis:903.1 ; Mathematics:921

Web of Science

South Univ Sci & Technol China, Dept Elect & Elect Engn, Shenzhen, Peoples R China

Fang, Zhiyuan,Zhang, Lingqi,Chen, Kun. A Behavior Mining Based Hybrid Recommender System[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2016:1-5.