Abstract
Textual contents associated to resources are considered as sources of candidate tags to improve the performance of tag recommenders in social tagging systems. In this paper, we propose a two-level learning hierarchy of a concept based keyword extraction method to filter the candidate tags and rank them based on their occurrences in concepts existing in the given resources. Incorporating user-created tags to extract the hidden concept-document relationships distinguishes the two-level from the one-level learning version, which extracts concepts directly using terms existing in textual contents. Our experiment shows that a multi-concept approach, which considers more than one concept for each resource, improves the performance of a single-concept approach, which takes into account just the most relevant concept. Moreover, the experiments also prove that the proposed two-level learning hierarchy gives better performances than one of the one-level version.
Original language | English |
---|---|
Pages (from-to) | 201-214 |
Number of pages | 14 |
Journal | CEUR Workshop Proceedings |
Volume | 497 |
Publication status | Published - 2009 |
Event | International Workshop on Discovery Challenge, DC 2009 at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2009 - Bled, Slovenia Duration: 7 Sept 2009 → 7 Sept 2009 |
Keywords
- Concept extraction
- Keyword extraction
- Machine learning
- Recommender system
- Social tagging