A Survey of Matrix Completion Methods for Recommendation Systems
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...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2018-12-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2018.9020008 |
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| _version_ | 1832568963659202560 |
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| author | Andy Ramlatchan Mengyun Yang Quan Liu Min Li Jianxin Wang Yaohang Li |
| author_facet | Andy Ramlatchan Mengyun Yang Quan Liu Min Li Jianxin Wang Yaohang Li |
| author_sort | Andy Ramlatchan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1e8367e4732a4cde9830ee08f09de347 |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2018-12-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-1e8367e4732a4cde9830ee08f09de3472025-02-02T23:47:25ZengTsinghua University PressBig Data Mining and Analytics2096-06542018-12-011430832310.26599/BDMA.2018.9020008A Survey of Matrix Completion Methods for Recommendation SystemsAndy Ramlatchan0Mengyun Yang1Quan Liu2Min Li3Jianxin Wang4Yaohang Li5<institution>NASA Langley Research Center</institution>, <city>Hampton</city>, <state>VA</state> <postal-code>23666</postal-code>, <country>USA</country> and the <institution content-type="dept">Department of Computer Science</institution>, <institution>Old Dominion University</institution>, <city>Norfolk</city>, <state>VA</state> <postal-code>23666</postal-code>, <country>USA</country>.<institution content-type="dept">Department of Computer Science</institution>, <institution>Central South University</institution>, <city>Changsha</city> <postal-code>410083</postal-code>, <country>China</country> and the <institution content-type="dept">Department of Science</institution>, <institution>Shaoyang University</institution>, <city>Shaoyang </city><postal-code>422000</postal-code>, <country>China</country>.<institution content-type="dept">Department of Computer Science</institution>, <institution>Central South University</institution>, <city>Changsha</city> <postal-code>410083</postal-code>, <country>China</country>.<institution content-type="dept">Department of Computer Science</institution>, <institution>Central South University</institution>, <city>Changsha</city> <postal-code>410083</postal-code>, <country>China</country>.<institution content-type="dept">Department of Computer Science</institution>, <institution>Central South University</institution>, <city>Changsha</city> <postal-code>410083</postal-code>, <country>China</country>.<institution content-type="dept">Department of Computer Science</institution>, <institution>Old Dominion University</institution>, <city>Norfolk</city>, <state>VA</state> <postal-code>23529</postal-code>, <country>USA</country>.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.https://www.sciopen.com/article/10.26599/BDMA.2018.9020008matrix completioncollaborative filteringrecommendation systems |
| spellingShingle | Andy Ramlatchan Mengyun Yang Quan Liu Min Li Jianxin Wang Yaohang Li A Survey of Matrix Completion Methods for Recommendation Systems Big Data Mining and Analytics matrix completion collaborative filtering recommendation systems |
| title | A Survey of Matrix Completion Methods for Recommendation Systems |
| title_full | A Survey of Matrix Completion Methods for Recommendation Systems |
| title_fullStr | A Survey of Matrix Completion Methods for Recommendation Systems |
| title_full_unstemmed | A Survey of Matrix Completion Methods for Recommendation Systems |
| title_short | A Survey of Matrix Completion Methods for Recommendation Systems |
| title_sort | survey of matrix completion methods for recommendation systems |
| topic | matrix completion collaborative filtering recommendation systems |
| url | https://www.sciopen.com/article/10.26599/BDMA.2018.9020008 |
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