Journal of Information Technology Applications and Management

Korea Data Strategy Society (한국데이터전략학회)
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Improvement of Item-Based Collaborative Filtering by Applying Each Customer's Purchase Patterns in Offline Shopping Malls

오프라인 쇼핑몰에서 고객의 과거 구매 패턴을 활용한 아이템 기반 협업필터링 성능 개선에 관한 연구

  • Received : 2017.04.06
  • Accepted : 2017.07.11
  • Published : 2017.12.31
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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.

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References

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