SVR-based collaborative filtering algorithm: From dense to sparse

Article type: Research Article

Authors: Hui, Kanghua^* | Ji, Yu | Wang, Jin

Affiliations: College of Computer Science and Technology, Civil Aviation University of China, Tianjin 300300, China

Correspondence: [*] Corresponding author: Kanghua Hui, College of Computer Science and Technology, Civil Aviation University of China, Tianjin 300300, China. E-mail: [email protected].

Abstract: Recommender systems have been very important components to prevent people from dwelling in the overwhelming information. In this paper we analyze the difference between item-based recommendation algorithms and SVR-based collaborative filtering algorithms, and it can be found that item-based method performs much better while the data is not sparse significantly, and SVR-based method performs better while the data is dense and small. On this premise we propose a method that can combine the advantages of these two methods by predicting a small part of ratings using SVR method firstly and then predicting the rest of ratings using the item-based algorithm, which can solve the problem of data sparsity to certain extend. Finally, we evaluate our results compared with the benchmark on different datasets and prove our method’s advantages.

Keywords: Support vector regression, item-based recommendation, collaborative filtering, data sparsity

DOI: 10.3233/JCM-193767

Journal: Journal of Computational Methods in Sciences and Engineering, vol. 19, no. 4, pp. 1055-1063, 2019