Incorporating singular value decomposition in user-based collaborative filtering technique for a movie recommendation system: A comparative study
Document Type
Conference Proceeding
Publication Date
8-26-2019
Abstract
User-based collaborative filtering (UCF) technique is typically used to build a recommendation system (RS). A wide variety of techniques, such as matrix factorization, cosine similarity and Pearson correlation, have been proposed to improve the performance of the UCF algorithm in order to build more intelligent RSs. In this paper, we first describe the traditional UCF algorithm as the baseline; then we apply various techniques including singular value decomposition (SVD), cosine similarity, and Pearson correlation to examine and compare the performance of a small-scale movie RS. Our preliminary experimental results show that the UCF which used SVD and Pearson correlation performs better than a traditional UCF.
Publication Title
ACM International Conference Proceeding Series
First Page Number
12
Last Page Number
15
DOI
10.1145/3357777.3357782
Recommended Citation
Chen, Vito Xituo and Tang, Tiffany Y., "Incorporating singular value decomposition in user-based collaborative filtering technique for a movie recommendation system: A comparative study" (2019). Kean Publications. 1324.
https://digitalcommons.kean.edu/keanpublications/1324