Name
Abstract | ||
|---|---|---|
Item-based collaborative filtering is becoming the most promising approach in recommender systems. It can predict an active user's interest for a target item based on his observed ratings. With the user's interests changing during interacting with collaborative filtering, the issue of concept drift is becoming a main factor impacting the accuracy of recommendation. Aiming at the issue of concept drift, we propose a temporal item-based collaborative filtering approach. in which the temporal weight is employed in both similarity computing and rating prediction. As the experimental result shows, the proposed approach improves the quality of recommendation in contrast to the classic item-based collaborative filtering. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-27183-0_44 | Communications in Computer and Information Science |
Keywords | Field | DocType |
Recommender system,Item-based collaborative filtering,concept drift,temporal similarity,temporal prediction | Recommender system,Collaborative filtering,Pattern recognition,Computer science,Concept drift,Artificial intelligence,Machine learning,Temporal similarity | Conference |
Volume | Issue | ISSN |
260 | null | 1865-0929 |
Citations | PageRank | References |
1 | 0.36 | 11 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lei Ren | 1 | 13 | 2.66 |
| Junzhong Gu | 2 | 113 | 34.92 |
| Weiwei Xia | 3 | 28 | 14.30 |