Discovery of Hidden Similarity on Collaborative Filtering to Overcome Sparsity Problem

1. Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: SIGIR 1999: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Berkeley, CA, pp. 230–237. ACM Press, New York (1999)

   Chapter  Google Scholar 

2. Polcicova, G., Slovak, R., Navrat, P.: Combining content-based and collaborative filtering. In: ADBIS-DASFAA Symposium, pp. 118–127 (2000)

   Google Scholar 

3. Linden, G., Smith, B., York, J.: Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing 7, 76–80 (2003)

   Article  Google Scholar 

4. Melville, P., Mooney, R.J., Nagarajan, R.: Content-boosted collaborative filtering for improved recommendations. In: Proceedings of the Eighteenth National Conference on Artificial Intelligence, Edmonton, Canada, pp. 187–192 (2002)

   Google Scholar 

5. Maneeroj, S., Kanai, H., Hakozaki, K.: Combining dynamic agents and collaborative filtering without sparsity rating problem for better recommendation quality. In: Proceedings of the Second DELOS Network of Excellence Workshop on Personalisation and Recommender Systems in Digital Libraries (2001)

   Google Scholar 

6. O’Connor, M., Herlocker, J.: Clustering items for collaborative filtering. In: Recommender Systems Workshop at 1999 Conference on Research and Development in Information Retrieval, Berkeley, CA (1999)

   Google Scholar 

7. Heckerman, D., Kadie, C., Breese, J.S.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the Fourteenth Conference, Uncertainty in Artificial Intelligence, pp. 43–52 (1998)

   Google Scholar 

8. Ariyoshi, Y.: Improvement of combination information filtering method based on reliabilities. IPSJ SIGNotes Fundamental Infology (1998)

   Google Scholar 

9. Vozalis, E., Margaritis, K.G.: Analysis of recommender systems’ algorithms. In: Proceedings of the Sixth Hellenic-European Conference on Computer Mathematics and its Applications - HERCMA 2003 (2003)

   Google Scholar 

10. Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: World Wide Web, pp. 285–295 (2001)

    Google Scholar 

11. Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Application of dimensionality reduction in recommender systems – a case study. In: ACM WebKDD 2000 Web Mining for E-Commerce Workshop (2000)

    Google Scholar 

12. Kohrs, A., Merialdo, B.: Clustering for collaborative filtering applications (1999)

    Google Scholar 

13. Good, N., Ben Schafer, J., Konstan, J.A., Borchers, A., Sarwar, B., Herlocker, J., Riedl, J.: Combining collaborative filtering with personal agents for better recommendations. In: Proceedings of the 6th National Conference on Artificial Intelligence (AAAI 1999); Proceedings of the 11th Conference on Innovative Applications of Artificial Intelligence, pp. 439–446 (1999)

    Google Scholar 

14. Pazzani, M.J.: A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Review 13, 393–408 (1999)

    Article  Google Scholar 

15. Billsus, D., Pazzani, M.J.: Learning collaborative information filters. In: Proceedings of the Fifteenth International Conference on Machine Learning (ICML 1998), pp. 46–54. Morgan Kaufmann, San Francisco (1998)

    Google Scholar 

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