Content-boosted collaborative filtering using semantic similarity measure

Ceylan U., Birturk A.

7th International Conference on Web Information Systems and Technologies, WEBIST 2011, Noordwijkerhout, Hollanda, 6 - 09 Mayıs 2011, ss.366-371, (Tam Metin Bildiri) identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Basıldığı Şehir: Noordwijkerhout
  • Basıldığı Ülke: Hollanda
  • Sayfa Sayıları: ss.366-371
  • Anahtar Kelimeler: Content-boosted collaborative filtering, Item cold-start, Ontology, Semantic similarity, Sparsity
  • TED Üniversitesi Adresli: Hayır

Özet

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.