A Comparison of Collaborative-Filtering Recommendation Algorithms for E-commerce

Author

Huang, Zan ; Zeng, Daniel ; Chen, Hsinchun

Author_Institution

Pennsylvania State Univ, James City

Volume

22

Issue

5

fYear

2007

Firstpage

68

Lastpage

78

Abstract

Collaborative filtering is one of the most widely adopted and successful recommendation approaches. Unlike approaches based on intrinsic consumer and product characteristics, CF characterizes consumers and products implicitly by their previous interactions. The simplest example is to recommend the most popular products to all consumers. Researchers are advancing CF technologies in such areas as algorithm design, human- computer interaction design, consumer incentive analysis, and privacy protection.

Keywords

electronic commerce; groupware; information filtering; information filters; algorithm design; collaborative-filtering recommendation algorithms e-commerce; consumer incentive analysis; human- computer interaction design; intrinsic consumer; privacy protection; product characteristics; recommendation approaches; Aggregates; Algorithm design and analysis; Collaboration; Feedback; Filtering algorithms; Guidelines; Optical wavelength conversion; Prediction algorithms; Privacy; Protection; algorithm design and evaluation; collaborative filtering; e-commerce; recommender systems;

fLanguage

English

Journal_Title

Intelligent Systems, IEEE

Publisher

ieee

ISSN

1541-1672

Type

jour

DOI

10.1109/MIS.2007.4338497

Filename

4338497