Ugur Ceylan, Aysenur Birturk


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.


  1. Bach, T. L. and Kuntz, R. D. (2005). Measuring similarity of elements in owl dl ontologies. In Proceedings of the AAAI'05 Workshop on Contexts and Ontologies: Theory, Practice and Applications, pages 96-99.
  2. Balabanovíc, M. and Shoham, Y. (1997). Fab: Contentbased, collaborative recommendation. Communications of the ACM, 40(3):66-72.
  3. Breese, J. S., Heckerman, D., and Kadie, C. (1998). Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI98), pages 43-52, San Francisco. Morgan Kaufmann.
  4. Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., and Sartin, M. (1999). Combining contentbased and collaborative filters in an online newspaper. In Proceedings of ACM SIGIR Workshop on Recommender Systems.
  5. Herlocker, J. L., Konstan, J. A., Borchers, A., and Riedl, J. (1999). An algorithmic framework for performing collaborative filtering. In Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, pages 230-237, New York, NY, USA. ACM Press.
  6. Karaman, H. (2010). Content based movie recommendation system empowered by collaborative missing data prediction. M.Sc. Thesis in Computer Engineering Department of Middle East Technical University.
  7. Li, Y., Bandar, Z. A., and McLean, D. (2003). An approach for measuring semantic similarity between words using multiple information sources. IEEE Transactions on Knowledge and Data Engineering, 15:871-882.
  8. Lin, D. (1998). An information-theoretic definition of similarity. In ICML 7898: Proceedings of the Fifteenth International Conference on Machine Learning, pages 296-304, San Francisco, CA, USA. Morgan Kaufmann Publishers Inc.
  9. Lula, P. and Paliwoda-Pekosz, G. (2008). An ontologybased cluster analysis framework. In 7th International Semantic Web Conference (ISWC2008).
  10. Maedche, A. and Zacharias, V. (2002). Clustering ontologybased metadata in the semantic web. In Elomaa, T., Mannila, H., and Toivonen, H., editors, Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery (PKDD 2002), August 19-23, 2002, Helsinki, Finland, volume 2431 of Lecture Notes in Computer Science, pages 383-408. Springer, Berlin-Heidelberg, Germany.
  11. Maimon, O. (2005). Decomposition Methodology For Knowledge Discovery And Data Mining: Theory And Applications (Machine Perception and Artificial Intelligence). World Scientific Publishing Co., Inc., River Edge, NJ, USA.
  12. Melville, P., Mooney, R. J., and Nagarajan, R. (2002). Content-boosted collaborative filtering for improved recommendations. In Proceedings of the Eighteenth National Conference on Artificial Intelligence, pages 187-192.
  13. Mitchell, T. M. (1997). Machine Learning. McGraw-Hill International Edit.
  14. Pazzani, M. J. (1999). A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Review, 13:393-408.
  15. Sarwar, B., Karypis, G., Konstan, J., and Reidl, J. (2001). Item-based collaborative filtering recommendation algorithms. In WWW 7801: Proceedings of the 10th international conference on World Wide Web, pages 285- 295, New York, NY, USA. ACM.
  16. Sarwar, B. M., Karypis, G., Konstan, J. A., and Riedl, J. T. (2000). Application of dimensionality reduction in recommender systems: A case study. In WebKDD Workshop at the ACM SIGKKD.
  17. Tintarev, N. and Masthoff, J. (2006). Similarity for news recommender systems. In Proceedings of the AH06 Workshop on Recommender Systems and Intelligent User Interfaces.
  18. Wu, Z. and Palmer, M. (1994). Verb semantics and lexical selection. In Proc. of the 32nd annual meeting on Association for Computational Linguistics, pages 133-138.

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

author={Ugur Ceylan and Aysenur Birturk},
booktitle={Proceedings of the 7th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},

in EndNote Style

JO - Proceedings of the 7th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
SN - 978-989-8425-51-5
AU - Ceylan U.
AU - Birturk A.
PY - 2011
SP - 366
EP - 371
DO - 10.5220/0003402403660371