Name
Abstract | ||
|---|---|---|
This paper focuses on the utilization of the history of navigation within recommender systems. It aims at designing a collaborative recommender based on Markov models relying on partial matching in order to ensure high accuracy, coverage, robustness, low complexity while being anytime. Indeed, contrary to state of the art, this model does not simply match the context of the active user to the context of other users but partial matching is performed: the history of navigation is divided into several sub-histories on which matching is performed, allowing the matching constraints to be weakened. The resulting model leads to an improvement in terms of accuracy compared to state of the art models. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-02247-0_34 | UMAP |
Keywords | Field | DocType |
history dependent recommender systems,markov model,active user,matching constraint,partial matching,low complexity,recommender system,collaborative recommender,art model,resulting model,high accuracy | Recommender system,Data mining,Collaborative filtering,Computer science,Markov model,Robustness (computer science),Association rule learning,Artificial intelligence,Machine learning | Conference |
Volume | ISSN | Citations |
5535 | 0302-9743 | 3 |
PageRank | References | Authors |
0.40 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Armelle Brun | 1 | 138 | 21.49 |
| Geoffray Bonnin | 2 | 58 | 6.29 |
| Anne Boyer | 3 | 19 | 5.24 |