A COLLABORATIVE FILTERING APPROACH COMBINING CLUSTERING AND NAVIGATIONAL BASED CORRELATIONS

Ilham Esslimani, Armelle Brun, Anne Boyer

2009

Abstract

Recommender systems are widely used for automatic personalization of information on web sites and information retrieval systems. Collaborative Filtering (CF) is the most popular recommendation technique, but several CF systems still suffer from problems like data rating availability and space dimensionality for neighborhood selection. In this paper, we present a new CF approach (PSN-CF) that uses usage traces to model users. These traces are used to estimate ratings that will be employed to generate clusters. Then, the PSN-CF evaluates navigational correlations between users within these clusters. Predictions are performed in a following step. The performance of PSN-CF is evaluated in terms of accuracy and time processing on a real usage dataset. We show that PSN-CF highly improves the accuracy of predictions in terms of MAE. Moreover, the use of clustering and positive sequences before computing the navigational correlations contributes to an important reduction of time processing.

References

  1. Baltrunas, L. and Ricci, F. (2007). Dynamic item weighting and selection for collaborative filtering. In Web mining 2.0 Workshop, ECML-PKDD 2007. Springer-Verlag.
  2. Banerjee, A. and Ghosh, J. (2001). Clickstream clustering using weighted longest common subsequences. In Proceedings of the Web Mining Workshop at the 1st SIAM Conference on Data Mining.
  3. Castagnos, S. (2008). Modélisation de comportements et apprentissage stochastique non supervisé de stratégies d'interactions sociales au sein de systémes temps réel de recherche et d'accés à l'information. PhD thesis, Nancy 2 University, France.
  4. Conner, M. and Herlocker, J. (1999). Clustering items for collaborative filtering. In Proceedings of the ACM SIGIR Workshop on Recommender Systems.
  5. Eirinaki, M., Vazirgiannis, M., and Kapogiannis, D. (2005). Web path recommendations based on page ranking and markov models. In Proceedings of the 7th annual ACM international workshop on Web information and data management. ACM Press.
  6. George, T. and Merugu, S. (2005). A scalable collaborative filtering framework based on co-clustering. In Proceedings of the Fifth IEEE International Conference on Data Mining. IEEE Computer Society.
  7. Gery, M. and Haddad, H. (2003). Evaluation of web usage mining approaches for user's next request prediction. In Proceedings of the 5th ACM international workshop on Web information and data management. ACM Press.
  8. Herlocker, J., Konstan, J., 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.
  9. Jalali, M., Mustapha, N., Sulaiman, N., and Mamat, A. (2008). A web usage mining approach based on lcs algorithm in online predicting recommendation systems. In Proceedings of 12th conference of information visualisation.
  10. Jiang, X., Song, W., and Feng, W. (2006). Optimizing collaborative filtering by interpolating the individual and group behaviors. In APWeb.
  11. Kaufman, L. and Rousseeuw, P. (1990). Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley and Sons, New York.
  12. Xue, G., Lin, C., and Yang, Q. (2005). Scalable collaborative filtering using cluster-based smoothing. In Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval.
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Paper Citation


in Harvard Style

Esslimani I., Brun A. and Boyer A. (2009). A COLLABORATIVE FILTERING APPROACH COMBINING CLUSTERING AND NAVIGATIONAL BASED CORRELATIONS . In Proceedings of the Fifth International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 978-989-8111-81-4, pages 364-369. DOI: 10.5220/0001841303640369


in Bibtex Style

@conference{webist09,
author={Ilham Esslimani and Armelle Brun and Anne Boyer},
title={A COLLABORATIVE FILTERING APPROACH COMBINING CLUSTERING AND NAVIGATIONAL BASED CORRELATIONS},
booktitle={Proceedings of the Fifth International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},
year={2009},
pages={364-369},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001841303640369},
isbn={978-989-8111-81-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Fifth International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - A COLLABORATIVE FILTERING APPROACH COMBINING CLUSTERING AND NAVIGATIONAL BASED CORRELATIONS
SN - 978-989-8111-81-4
AU - Esslimani I.
AU - Brun A.
AU - Boyer A.
PY - 2009
SP - 364
EP - 369
DO - 10.5220/0001841303640369