Document Type
Article
Publication Date
7-2018
DOI
10.26599/BDMA.2018.9020008
Publication Title
Big Data Mining and Analytics
Volume
1
Issue
4
Pages
308-323
Abstract
In recent years, the recommendation systems have become increasingly popular and have been used in a broad variety of applications. Here, we investigate the matrix completion techniques for the recommendation systems that are based on collaborative filtering. The collaborative filtering problem can be viewed as predicting the favorability of a user with respect to new items of commodities. When a rating matrix is constructed with users as rows, items as columns, and entries as ratings, the collaborative filtering problem can then be modeled as a matrix completion problem by filling out the unknown elements in the rating matrix. This article presents a comprehensive survey of the matrix completion methods used in recommendation systems. We focus on the mathematical models for matrix completion and the corresponding computational algorithms as well as their characteristics and potential issues. Several applications other than the traditional user-item association prediction are also discussed.
Original Publication Citation
Ramlatchan, A., Yang, M., Liu, Q., Li, M., Wang, J., & Li, Y. (2018). A survey of matrix completion methods for recommendation systems. Big Data Mining and Analytics, 1(4), 308-323. doi:10.26599/BDMA.2018.9020008
Repository Citation
Ramlatchan, A., Yang, M., Liu, Q., Li, M., Wang, J., & Li, Y. (2018). A survey of matrix completion methods for recommendation systems. Big Data Mining and Analytics, 1(4), 308-323. doi:10.26599/BDMA.2018.9020008
Comments
Big Data Mining and Analytics is an Open Access only journal. Open Access provides unrestricted online access.