Abstract
In this paper, we propose a spectral clustering approach for users and documents group modeling in order to capture the common preference and relatedness of users and documents, and to reduce the time complexity of similarity calculations. In experiments, we investigate the selection of the optimal amount of clusters. We also show a reduction of the time consuming in calculating the similarity for the recommender systems by selecting a centroid first, and then compare the inside item on behalf of each group.
| Original language | English |
|---|---|
| Title of host publication | Proceeding of The 18th Intl. Workshop on Personalization and Recommendation on the Web and Beyond |
| Publisher | University of Kassel |
| Publication date | Oct 2010 |
| Pages | 315-321 |
| Chapter | 6 |
| Publication status | Published - Oct 2010 |
| Event | LWA 2010 - Kassel, Germany Duration: 4 Oct 2010 → 6 Oct 2010 |
Conference
| Conference | LWA 2010 |
|---|---|
| Country/Territory | Germany |
| City | Kassel |
| Period | 04/10/2010 → 06/10/2010 |
Bibliographical note
@article{panuser,title={{User and Document Group Approach of Clustering in Tagging Systems}},
author={Pan, R. and Xu, G. and Dolog, P.}
}
Keywords
- User Profile
- Document Profile
- Spectral Clustering
- Group Profile
- Modularity Metric