Paper Info
Title
A hybrid online-product recommendation system: Combining implicit rating-based collaborative filtering and sequential pattern analysis
Abstract
Many online shopping malls in which explicit rating information is not available still have difficulty in providing recommendation services using collaborative filtering (CF) techniques for their users. Applying temporal purchase patterns derived from sequential pattern analysis (SPA) for recommendation services also often makes users unhappy with the inaccurate and biased results obtained by not considering individual preferences. The objective of this research is twofold. One is to derive implicit ratings so that CF can be applied to online transaction data even when no explicit rating information is available, and the other is to integrate CF and SPA for improving recommendation quality. Based on the results of several experiments that we conducted to compare the performance between ours and others, we contend that implicit rating can successfully replace explicit rating in CF and that the hybrid approach of CF and SPA is better than the individual ones.
Year
DOI
Venue
2012
10.1016/j.elerap.2012.02.004
Electronic Commerce Research and Applications
Keywords
Field
DocType
individual preference,temporal purchase pattern,sequential pattern analysis,implicit rating,explicit rating information,hybrid approach,explicit rating,hybrid online-product recommendation system,implicit rating-based collaborative,online shopping mall,recommendation service,recommendation quality,collaborative filtering
Recommender system,Collaborative filtering,Computer science,Pattern analysis,Transaction data,Marketing
Journal
Volume
Issue
ISSN
11
4
1567-4223
Citations 
PageRank 
References 
60
1.61
56
Authors
4
Name
Order
Citations
PageRank
Keunho Choi115310.18
Donghee Yoo2886.26
Gunwoo Kim3927.13
Yongmoo Suh417013.50