LEARNING NEIGHBOURHOOD-BASED COLLABORATIVE FILTERING PARAMETERS

J. Griffith, C. O'Riordan, H. Sorensen

2011

Abstract

The work outlined in this paper uses a genetic algorithm to learn the optimal set of parameters for a neighbourhood-based collaborative filtering approach. The motivation is firstly to re-assess whether the default parameter values often used are valid and secondly to assess whether different datasets require different parameter settings. Three datasets are considered in this initial investigation into the approach: Movielens, Bookcrossing and Lastfm.

References

  1. Bobadilla, J., Serradilla, F., and Bernal, J. (2010). A new collaborative filtering metric that improves the behaviour of recommender systems. Knowledge-Based Systems, 23:520-528.
  2. Breese, J., 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. Morgan Kaufmann.
  3. Herlocker, J., Konstan, J., and Riedl, J. (2002). An empirical analysis of design choices in neighbourhoodbased collaborative filtering algorithms. Information Retrieval, 5:287-310.
  4. Holland, J. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor.
  5. Howe, A. and Forbes, R. (2008). Re-considering neighbourhood-based collaborative filtering parameters in the context of new data. In Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM).
  6. Hwang, C.-S. (2010). Genetic algorithms for feature weighting in multi-criteria recommender systems. JCIT: Journal of Convergence Information Technology, 5(8):126-136.
  7. Ko, S. and Lee, J. (2002). User preference mining through collaborative filtering and content based filtering in recommender system. In Third International Conference on Electronic Commerece and Web Technologies (EC-Web), pages 244-253.
  8. Trotman, A. (2005). Learning to rank. Journal of Information Retrieval, 8(3).
  9. Ujjin, S. and Bentley, P. (2002). Learning user preferences using evolution. In 4th Asia-Pacific Conference on Simulated Evolution and Learning.
Download


Paper Citation


in Harvard Style

Griffith J., O'Riordan C. and Sorensen H. (2011). LEARNING NEIGHBOURHOOD-BASED COLLABORATIVE FILTERING PARAMETERS . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2011) ISBN 978-989-8425-79-9, pages 444-447. DOI: 10.5220/0003657404520455


in Bibtex Style

@conference{kdir11,
author={J. Griffith and C. O'Riordan and H. Sorensen},
title={LEARNING NEIGHBOURHOOD-BASED COLLABORATIVE FILTERING PARAMETERS},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2011)},
year={2011},
pages={444-447},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003657404520455},
isbn={978-989-8425-79-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2011)
TI - LEARNING NEIGHBOURHOOD-BASED COLLABORATIVE FILTERING PARAMETERS
SN - 978-989-8425-79-9
AU - Griffith J.
AU - O'Riordan C.
AU - Sorensen H.
PY - 2011
SP - 444
EP - 447
DO - 10.5220/0003657404520455