Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1155/2016/9564293 | INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS |
Field | DocType | Volume |
Recommender system,Data mining,Obstacle,Social network,Collaborative filtering,Matrix (mathematics),Regression analysis,Computer science,Network architecture,Correlation,Artificial intelligence,Machine learning | Journal | 2016 |
ISSN | Citations | PageRank |
1550-1477 | 0 | 0.34 |
References | Authors | |
10 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bailing Wang | 1 | 24 | 3.81 |
| Junheng Huang | 2 | 4 | 2.77 |
| Dongjie Zhu | 3 | 4 | 4.77 |
| Xilu Hou | 4 | 0 | 0.34 |