A probabilistic clothes recommender based on clothes features

Author

Hu Xiao-song ; Jiang Li-ling ; Cheng Rui ; Wang Tie-jun ; Li Qing

Author_Institution

Sch. of Econ. Inf. Eng., Southwestern Univ. of Finance & Econ., Chengdu, China

fYear

2014

fDate

17-19 Aug. 2014

Firstpage

76

Lastpage

81

Abstract

Due to its convenience and preferential price, online clothes-selling business grows up quickly and becomes one of the most profitable businesses in e-Commerce companies including Taobao.com and JD.com. Here, how to assist customers find their favorite clothes is an interesting challenge to these companies. In this article, we developed a probabilistic clothes recommendation system (HPRS) for easy shopping. One of the unique features of this system is the ability to recommend clothes in terms of both user ratings and clothes attributes. This is achieved by extracting key clothes features that influence the decision-making of online shopping from clothes images. Our experiments show the efficiency of the proposed algorithm.

Keywords

clothing; customer services; decision making; electronic commerce; feature extraction; marketing; JD.com; Taobao.com; clothes attributes; clothes feature extraction; decision making; e-commerce companies; online clothes selling business; online shopping; probabilistic clothes recommender system; Collaboration; Equations; Filtering; Image color analysis; Mathematical model; Predictive models; Probabilistic logic; collaborative filtering; information filtering; probabilistic model; recommender;

fLanguage

English

Publisher

ieee

Conference_Titel

Management Science & Engineering (ICMSE), 2014 International Conference on

Conference_Location

Helsinki

Print_ISBN

978-1-4799-5375-2

Type

conf

DOI

10.1109/ICMSE.2014.6930211

Filename

6930211