A survey of matrix completion methods for recommendation systems

• Published in: Big Data Mining and Analytics Vol. 1; no. 4; pp. 308 - 323
• Main Authors: Ramlatchan, Andy, Yang, Mengyun, Liu, Quan, Li, Min, Wang, Jianxin, Li, Yaohang
• Format: Journal Article
• Language: English
• Published: Beijing Tsinghua University Press 01.12.2018
• Subjects: Algorithms; Big Data; Collaboration; collaborative filtering; Computer science; Data mining; Filtering systems; Filtration; Machine learning; Mathematical models; matrix completion; Ratings & rankings; recommendation systems; Recommender systems; User behavior
• ISSN: 2096-0654; 2097-406X
• DOI: 10.26599/BDMA.2018.9020008

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.