Name
Abstract | ||
|---|---|---|
Collaborative filtering is a well-known technique for recommender systems. Collaborative filtering models use the available preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. Collaborative filtering suffers from the data sparsity problem when users only rate a small set of items which makes the computation of users similarity imprecise and reduce consequently the accuracy of the recommended items. Clustering techniques include multiplex network clustering can be used to deal with this problem. In this paper, we propose a collaborative filtering system based on clustering multiplex network that predict the rate value that a user would give to an item. This approach looks, in a first step, for users having the same behavior or sharing the same characteristics. Then, use the ratings from those similar users found in the first step to predict other ratings. The proposed approach has been validated on MovieLens dataset and the obtained results have shown very promising performances. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.procs.2018.10.524 | Procedia Computer Science |
Keywords | Field | DocType |
Community detection,Multiplex network,Recommender System,Collaborative Filleting | Recommender system,Data mining,Network clustering,Collaborative filtering,Computer science,MovieLens,Artificial intelligence,Cluster analysis,Small set,Machine learning,Computation | Conference |
Volume | ISSN | Citations |
144 | 1877-0509 | 0 |
PageRank | References | Authors |
0.34 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Issam Falih | 1 | 5 | 3.85 |
| Nistor Grozavu | 2 | 67 | 16.76 |
| Rushed Kanawati | 3 | 239 | 26.45 |
| Younès Bennani | 4 | 269 | 53.18 |