콘텐츠 본문
논문 해외 국제전문학술지(SCI급) Extended Collaborative Filtering Technique for Mitigating the Sparsity Problem
- 학술지 구분 국제전문학술지(SCI급)
- 게재년월 2016
- 저자명 Keunho Choi, Yongmoo Suh, Donghee Yoo
- 학술지명 International Journal of Computers Communications and Control
- 발행처명 CCC PUBL-AGORA UNIV
- 발행국가 해외
- 논문언어 외국어
- 전체저자수 3
논문 초록 (Abstract)
Many online shopping malls have implemented personalized recommendation systems to improve customer retention in the age of high competition and information overload. Sellers make use of these recommendation systems to survive high competition and buyers utilize them to find proper product information for their own needs. However, transaction data of most online shopping malls prevent us from using collaborative filtering (CF) technique to recommend products, for the following two reasons: 1) explicit rating information is rarely available in the transaction data; 2) the sparsity problem usually occurs in the data, which makes it difficult to identify reliable neighbors, resulting in less effective recommendations. Therefore, this paper first suggests a means to derive implicit rating information from the transaction data of an online shopping mall and then proposes a new user similarity function to mitigate the sparsity problem. The new user similarity function computes the user similarity of two users if they rated similar items, while the user similarity function of traditional CF technique computes it only if they rated common items. Results from several experiments using an online shopping mall dataset in Korea demonstrate that our approach significantly outperforms the traditional CF technique.
