Name
Abstract | ||
|---|---|---|
Due to the uneven distributions of the ratings and social relationships for each user, two types of above recommendation methods should have varying weights when make recommendations. In this paper, we propose a user adaptive hybrid recommendation model, which dynamically combines a trust-aware based method and low-rank matrix factorization with adaptive tradeoff parameters, named as DTMF. It can utilize the advantages of these two methods and learn combinative parameters automatically. We investigate our model's performance on two social data sets - Epinions and Flixster. Experimental results show that DTMF performs better than other state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-21042-1_34 | WEB-AGE INFORMATION MANAGEMENT (WAIM 2015) |
Keywords | Field | DocType |
Recommender systemsa, Trust-aware, Matrix factorization | Data mining,Data set,Social relationship,Computer science,Matrix decomposition,Artificial intelligence,Machine learning,Recommendation model | Conference |
Volume | ISSN | Citations |
9098 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenlong Yang | 1 | 0 | 0.68 |
| Jun Ma | 2 | 47 | 19.80 |
| Shanshan Huang | 3 | 13 | 2.21 |