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http://dx.doi.org/10.21219/jitam.2017.24.4.001

Improvement of Item-Based Collaborative Filtering by Applying Each Customer's Purchase Patterns in Offline Shopping Malls  

Jeong, Seok Bong (Department of Railway, Kyungil University)
Publication Information
Journal of Information Technology Applications and Management / v.24, no.4, 2017 , pp. 1-12 More about this Journal
Abstract
Item-based collaborative filtering (IBCF) is an important technology that is widely used in recommender system of online shopping malls. It uses historical information to compute item-item similarity and make predictions. However, in offline shopping each customer's purchasing pattern can be occurred continuously and repeatedly due to time and space constraints contrast to online shopping. Those facts can make IBCF to have limitations from being applied to offline shopping malls directly. In order to improve the quality of recommendations made by IBCF in offline shopping mall, we propose an ensemble approach that considers both item-item similarity of IBCF and each customer's purchasing patterns which are modeled by item networks. Our experimental results show that this approach produces recommendation results superior to those of existing works such as pure IBCF or bestseller approaches.
Keywords
Recommender System; Collaborative Filtering; Offline Shopping; Item Based Collaborative Filtering;
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Times Cited By KSCI : 5  (Citation Analysis)
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