Name
Abstract | ||
|---|---|---|
Recommendation systems provide the facility to understand a person's taste and find new, desirable content for them based on aggregation between their likes and rating of different items. In this paper, we propose a recommendation system that predict the note given by a user to an item. This recommendation system is mainly based on unsupervised topological learning. The proposed approach has been validated on MovieLens dataset and the obtained results have show very promising performances. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-26535-3_26 | ICONIP |
Field | DocType | Volume |
Recommender system,Topology,Computer science,MovieLens,Artificial intelligence,Machine learning | Conference | 9490 |
ISSN | Citations | PageRank |
0302-9743 | 1 | 0.36 |
References | Authors | |
10 | 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 |