A Recommendation System Based on Regression Model of Three-Tier Network Architecture
The sparsity problem of user-item matrix is a major obstacle to improve the accuracy of the traditional collaborative filtering systems, and, meanwhile, it is also responsible for cold-start problem in the collaborative filtering approaches. In this paper, a three-tier network Architecture, which in...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi - SAGE Publishing
2016-03-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2016/9564293 |
| _version_ | 1797724457198944256 |
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| author | Wang Bailing Huang Junheng Zhu Dongjie Hou Xilu |
| author_facet | Wang Bailing Huang Junheng Zhu Dongjie Hou Xilu |
| author_sort | Wang Bailing |
| collection | DOAJ |
| description | The sparsity problem of user-item matrix is a major obstacle to improve the accuracy of the traditional collaborative filtering systems, and, meanwhile, it is also responsible for cold-start problem in the collaborative filtering approaches. In this paper, a three-tier network Architecture, which includes user relationship network, item similarity network, and user-item relationship network, is constructed using comprehensive data among the user-item matrix and the social networks. Based on this framework, a Regression Model Recommendation Approach (RMRA) is established to calculate the correlation score between the test user and test item. The correlation score is used to predict the test user preference for the test item. The RMRA mines the potential information among both social networks and user-item matrix to improve the recommendation accuracy and ease the cold-start problem. We conduct experiment based on KDD 2012 real data set. The result indicates that our algorithm performs superiorly compared to traditional collaborative filtering algorithm. |
| first_indexed | 2024-03-12T10:17:24Z |
| format | Article |
| id | doaj.art-1ce572ac815d483789aa6a39667dfd88 |
| institution | Directory Open Access Journal |
| issn | 1550-1477 |
| language | English |
| last_indexed | 2024-03-12T10:17:24Z |
| publishDate | 2016-03-01 |
| publisher | Hindawi - SAGE Publishing |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj.art-1ce572ac815d483789aa6a39667dfd882023-09-02T10:23:29ZengHindawi - SAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772016-03-011210.1155/2016/95642939564293A Recommendation System Based on Regression Model of Three-Tier Network ArchitectureWang BailingHuang JunhengZhu DongjieHou XiluThe sparsity problem of user-item matrix is a major obstacle to improve the accuracy of the traditional collaborative filtering systems, and, meanwhile, it is also responsible for cold-start problem in the collaborative filtering approaches. In this paper, a three-tier network Architecture, which includes user relationship network, item similarity network, and user-item relationship network, is constructed using comprehensive data among the user-item matrix and the social networks. Based on this framework, a Regression Model Recommendation Approach (RMRA) is established to calculate the correlation score between the test user and test item. The correlation score is used to predict the test user preference for the test item. The RMRA mines the potential information among both social networks and user-item matrix to improve the recommendation accuracy and ease the cold-start problem. We conduct experiment based on KDD 2012 real data set. The result indicates that our algorithm performs superiorly compared to traditional collaborative filtering algorithm.https://doi.org/10.1155/2016/9564293 |
| spellingShingle | Wang Bailing Huang Junheng Zhu Dongjie Hou Xilu A Recommendation System Based on Regression Model of Three-Tier Network Architecture International Journal of Distributed Sensor Networks |
| title | A Recommendation System Based on Regression Model of Three-Tier Network Architecture |
| title_full | A Recommendation System Based on Regression Model of Three-Tier Network Architecture |
| title_fullStr | A Recommendation System Based on Regression Model of Three-Tier Network Architecture |
| title_full_unstemmed | A Recommendation System Based on Regression Model of Three-Tier Network Architecture |
| title_short | A Recommendation System Based on Regression Model of Three-Tier Network Architecture |
| title_sort | recommendation system based on regression model of three tier network architecture |
| url | https://doi.org/10.1155/2016/9564293 |
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