Name
Abstract | ||
|---|---|---|
Traditional collaborative filtering algorithms all suffer from inaccurate recommendation and bad scalability. In this paper, we propose a new collaborative filtering algorithm based on user¿s interest partition. We divides user¿s whole interest into pieces. Each piece of interest is called interest unit. And the similarity between users on interest unit is named local similarity. The similarity between users on whole interest is named holistic similarity which is similar with the traditional similarity. Our approach searches the nearest neighbors of active user according to the linear combination of local similarity and holistic similarity. Through experiments, the algorithm can solve the problem of high sparsity on user-item matrix. Our algorithm also has a better quality on recommendation according to experiments. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ISCSCT.2008.37 | ISCSCT (1) |
Keywords | Field | DocType |
interest unit,interest partition,inaccurate recommendation,whole interest,active user,traditional similarity,holistic similarity,information filtering,new collaborative,traditional collaborative,recommender system,local similarity,local interest,new effective collaborative filtering,collaborative filtering,groupware,interest model,user interest partition,collaborative filtering algorithm,classification algorithms,clustering algorithms,collaboration,filtering | Recommender system,Data mining,Collaborative filtering,Information retrieval,Computer science,Collaborative software,Similarity heuristic,Filter (signal processing),Algorithm,Cluster analysis,Statistical classification,Scalability | Conference |
Volume | ISBN | Citations |
1 | 978-1-4244-3746-7 | 0 |
PageRank | References | Authors |
0.34 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| He Keqin | 1 | 5 | 0.81 |
| Liang He | 2 | 61 | 20.38 |
| Weiwei Xia | 3 | 28 | 14.30 |