• Journal of Internet Computing and Services
    ISSN 2287 - 1136(Online) / ISSN 1598 - 0170 (Print)
    http://jics.or.kr/

Collaborative Filtering using Co-Occurrence and Similarity information


Kwang Tek Na, Ju Hong Lee, Journal of Internet Computing and Services, Vol. 18, No. 3, pp. 19-28, Jun. 2017
10.7472/jksii.2017.18.3.19, Full Text:
Keywords: Collaborative Filtering, Recommender System, co occurrence, similarity, SVD, latent factor model

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.


Statistics
Show / Hide Statistics

Statistics (Cumulative Counts from November 1st, 2017)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.


Cite this article
[APA Style]
Kwang Tek Na and Ju Hong Lee (2017). Collaborative Filtering using Co-Occurrence and Similarity information. Journal of Internet Computing and Services, 18(3), 19-28. DOI: 10.7472/jksii.2017.18.3.19.

[IEEE Style]
K. T. Na and J. H. Lee, "Collaborative Filtering using Co-Occurrence and Similarity information," Journal of Internet Computing and Services, vol. 18, no. 3, pp. 19-28, 2017. DOI: 10.7472/jksii.2017.18.3.19.

[ACM Style]
Kwang Tek Na and Ju Hong Lee. 2017. Collaborative Filtering using Co-Occurrence and Similarity information. Journal of Internet Computing and Services, 18, 3, (2017), 19-28. DOI: 10.7472/jksii.2017.18.3.19.