DMCA
Discriminative Clustering for Content-Based Tag Recommendation in Social Bookmarking Systems
| Citations: | 3 - 3 self |
BibTeX
@MISC{Hassan_discriminativeclustering,
author = {Malik Tahir Hassan and Asim Karim and Suresh Man and James Cussens},
title = {Discriminative Clustering for Content-Based Tag Recommendation in Social Bookmarking Systems},
year = {}
}
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Abstract
Abstract. We describe and evaluate a discriminative clustering approach for content-based tag recommendation in social bookmarking systems. Our approach uses a novel and efficient discriminative clustering method that groups posts based on the textual contents of the posts. The method also generates a ranked list of discriminating terms for each cluster. We apply the clustering method to build two clustering models – one based on the tags assigned to posts and the other based on the content terms of posts. Given a new posting, a ranked list of tags and content terms is determined from the clustering models. The final tag recommendation is based on these ranked lists. If the poster’s tagging history is available then this is also utilized in the final tag recommendation. The approach is evaluated on data from BibSonomy, a social bookmarking system. Prediction results show that the tag-based clustering model is more accurate than the termbased clustering model. Combining the predictions from both models is better than either model’s predictions. Significant improvement in recommendation is obtained over the baseline method of recommending the most frequent tags for all posts. 1
Keyphrases
social bookmarking system content-based tag recommendation discriminative clustering ranked list content term final tag recommendation clustering method textual content frequent tag tag-based clustering model prediction result efficient discriminative clustering method discriminative clustering approach new posting clustering model baseline method model prediction group post termbased clustering model significant improvement
