Abstract
Clustering is useful in tag based recommenders to reduce sparsity of data and by doing so to improve also accuracy of recommendation. Strategy for the selection of tags for clusters has an impact on the accuracy. In this paper we propose a KNN based approach for ranking tag neighbors for tag selection. We study the approach in comparison to several baselines by using two datasets in different domains. We show, that in both cases the approach outperforms the compared approaches. Copyright © 2013 Springer-Verlag Berlin Heidelberg.
| Original language | English |
|---|---|
| Title of host publication | Agents and data mining interaction: 8th international workshop, ADMI 2012, Valencia, Spain, June 4-5, 2012, revised selected papers |
| Editors | Longbing CAO, Yifeng ZENG, Andreas L. SYMEONIDIS, Vladimir I. GORODETSKY, Philip S. YU, Munindar P SINGH |
| Publisher | Springer-Verlag Berlin Heidelberg |
| Pages | 115-125 |
| ISBN (Electronic) | 9783642362880 |
| ISBN (Print) | 9783642362873 |
| DOIs | |
| Publication status | Published - 2013 |
Citation
Pan, R., Dolog, P., & Xu, G. (2013). KNN-based clustering for improving social recommender systems. In L. Cao, Y. Zeng, A. L. Symeonidis, V. I. Gorodetsky, P. S. Yu, & M. P. Singh (Eds.), Agents and data mining interaction: 8th international workshop, ADMI 2012, Valencia, Spain, June 4-5, 2012, revised selected papers (pp. 115-125). Springer-Verlag Berlin Heidelberg. https://doi.org/10.1007/978-3-642-36288-0_11Keywords
- Tag neighbors
- Clustering
- Personalization
- Recommender systems
- Social tagging