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http://dx.doi.org/10.7472/jksii.2017.18.3.19

Collaborative Filtering using Co-Occurrence and Similarity information  

Na, Kwang Tek (Dept of Computer Science and Information Engineering, Inha University)
Lee, Ju Hong (Dept of Computer Science and Information Engineering, Inha University)
Publication Information
Journal of Internet Computing and Services / v.18, no.3, 2017 , pp. 19-28 More about this Journal
Abstract
Collaborative filtering (CF) is a system that interprets the relationship between a user and a product and recommends the product to a specific user. The CF model is advantageous in that it can recommend products to users with only rating data without any additional information such as contents. However, there are many cases where a user does not give a rating even after consuming the product as well as consuming only a small portion of the total product. This means that the number of ratings observed is very small and the user rating matrix is very sparse. The sparsity of this rating data poses a problem in raising CF performance. In this paper, we concentrate on raising the performance of latent factor model (especially SVD). We propose a new model that includes product similarity information and co occurrence information in SVD. The similarity and concurrence information obtained from the rating data increased the expressiveness of the latent space in terms of latent factors. Thus, Recall increased by 16% and Precision and NDCG increased by 8% and 7%, respectively. The proposed method of the paper will show better performance than the existing method when combined with other recommender systems in the future.
Keywords
Collaborative filtering; Recommender system; co occurrence; similarity; SVD; latent factor model;
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