CONTENT-BOOSTED COLLABORATIVE FILTERING USING SEMANTIC SIMILARITY MEASURE

Ugur Ceylan, Aysenur Birturk

Abstract

Collaborative filtering is one of the most used recommendation approaches in recommender systems. However, collaborative filtering systems have some major problems such as sparsity, scalability and cold-start problems. In this paper we focus on the sparsity and item cold-start problems in collaborative filtering in order to improve the quality of recommendations. We propose an approach that uses semantic similarities between items based on a priori defined ontology-based metadata in the movie domain. According to the semantic similarities between items and past user preferences, recommendations are made. The results of the evaluation phase show that our approach improves the quality of recommendations.

References

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Paper Citation


in Harvard Style

Ceylan U. and Birturk A. (2011). CONTENT-BOOSTED COLLABORATIVE FILTERING USING SEMANTIC SIMILARITY MEASURE . In Proceedings of the 7th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 978-989-8425-51-5, pages 366-371. DOI: 10.5220/0003402403660371


in Bibtex Style

@conference{webist11,
author={Ugur Ceylan and Aysenur Birturk},
title={CONTENT-BOOSTED COLLABORATIVE FILTERING USING SEMANTIC SIMILARITY MEASURE},
booktitle={Proceedings of the 7th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},
year={2011},
pages={366-371},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003402403660371},
isbn={978-989-8425-51-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - CONTENT-BOOSTED COLLABORATIVE FILTERING USING SEMANTIC SIMILARITY MEASURE
SN - 978-989-8425-51-5
AU - Ceylan U.
AU - Birturk A.
PY - 2011
SP - 366
EP - 371
DO - 10.5220/0003402403660371