Extended Collaborative Filtering Technique for Mitigating the Sparsity Problem

  • Keunho Choi
  • Yongmoo Suh
  • Donghee Yoo Gyeongsang National University

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.

Author Biography

Donghee Yoo, Gyeongsang National University
Assistant Professor, Department of Management Information Systems

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Published
2016-08-31
How to Cite
CHOI, Keunho; SUH, Yongmoo; YOO, Donghee. Extended Collaborative Filtering Technique for Mitigating the Sparsity Problem. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 11, n. 5, p. 631-644, aug. 2016. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/2152>. Date accessed: 03 july 2020. doi: https://doi.org/10.15837/ijccc.2016.5.2152.

Keywords

recommendation system, collaborative filtering, sparsity problem, similarity function