Using Collaborative Filtering to Overcome the Curse of Dimensionality when Clustering Users in a Group Recommender System

Ludovico Boratto, Salvatore Carta

2014

Abstract

A characteristic of most datasets is that the number of data points is much lower than the number of dimensions (e.g., the number of movies rated by a user is much lower than the number of movies in a dataset). Dealing with high-dimensional and sparse data leads to problems in the classification process, known as curse of dimensionality. Previous researches presented approaches that produce group recommendations by clustering users in contexts where groups are not available. In the literature it is widely-known that clustering is one of the classification forms affected by the curse of dimensionality. In this paper we propose an approach to remove sparsity from a dataset before clustering users in group recommendation. This is done by using a Collaborative Filtering approach that predicts the missing data points. In such a way, it is possible to overcome the curse of dimensionality and produce better clusterings. Experimental results show that, by removing sparsity, the accuracy of the group recommendations strongly increases with respect to a system that works on sparse data.

References

  1. Agrawal, R., Gehrke, J., Gunopulos, D., and Raghavan, P. (1998). Automatic subspace clustering of high dimensional data for data mining applications. In SIGMOD 1998, Proceedings ACM SIGMOD International Conference on Management of Data, pages 94-105. ACM Press.
  2. Amatriain, X., Jaimes, A., Oliver, N., and Pujol, J. M. (2011). Data mining methods for recommender systems. In Recommender Systems Handbook, pages 39- 71. Springer.
  3. Bellman, R. (1961). Adaptive control processes: a guided tour. Princeton University Press Princeton, N.J.
  4. Boratto, L. and Carta, S. (2011). State-of-the-art in group recommendation and new approaches for automatic identification of groups. In Information Retrieval and Mining in Distributed Environments, volume 324 of Studies in Computational Intelligence, pages 1-20. Springer Berlin Heidelberg.
  5. Boratto, L. and Carta, S. (2013). Exploring the ratings prediction task in a group recommender system that automatically detects groups. In IMMM 2013, The Third International Conference on Advances in Information Mining and Management, pages 36-43.
  6. Boumaza, A. M. and Brun, A. (2012). From neighbors to global neighbors in collaborative filtering: an evolutionary optimization approach. In Genetic and Evolutionary Computation Conference, GECCO 7812, pages 345-352. ACM.
  7. Chen, Y. and Pu, P. (2013). Cofeel: Using emotions to enhance social interaction in group recommender systems. In Alpine Rendez-Vous (ARV) 2013 Workshop on Tools and Technology for Emotion-Awareness in Computer Mediated Collaboration and Learning.
  8. DeSarbo, W. S., Carroll, J. D., Clark, L. A., and Green, P. E. (1984). Synthesized clustering: A method for amalgamating alternative clustering bases with differential weighting of variables. Psychometrika, 49(1):57-78.
  9. Goil, S., Nagesh, H., and Choudhary, A. (1999). Mafia: Efficient and scalable subspace clustering for very large data sets. Technical report, Northwestern University.
  10. Goren-Bar, D. and Glinansky, O. (2004). Fit-recommend ing tv programs to family members. Computers & Graphics, 28(2):149-156.
  11. Hinneburg, A. and Keim, D. A. (1999). Optimal gridclustering: Towards breaking the curse of dimensionality in high-dimensional clustering. In Proceedings of the 25th International Conference on Very Large Data Bases, VLDB 7899, pages 506-517. Morgan Kaufmann Publishers Inc.
  12. Huang, J. Z., Ng, M. K., Rong, H., and Li, Z. (2005). Automated variable weighting in k-means type clustering. IEEE Trans. Pattern Anal. Mach. Intell., pages 657- 668.
  13. Jameson, A. (2004). More than the sum of its members: challenges for group recommender systems. In Proceedings of the working conference on Advanced visual interfaces, AVI 2004, pages 48-54. ACM Press.
  14. Jameson, A. and Smyth, B. (2007). Recommendation to groups. In The adaptive web, pages 596-627. Springer-Verlag, Berlin, Heidelberg.
  15. Jing, L., Ng, M., and Huang, J. (2007). An entropy weighting k-means algorithm for subspace clustering of high-dimensional sparse data. Knowledge and Data Engineering, IEEE Transactions on, 19(8):1026- 1041.
  16. Jung, J. J. (2012). Attribute selection-based recommendation framework for short-head user group: An empirical study by movielens and imdb. Expert Systems with Applications, 39(4):4049-4054.
  17. Kanungo, T., Mount, D. M., Netanyahu, N. S., Piatko, C. D., Silverman, R., and Wu, A. Y. (2002). An efficient k-means clustering algorithm: Analysis and implementation. IEEE Trans. Pattern Anal. Mach. Intell., 24:881-892.
  18. Makarenkov, V. and Legendre, P. (2001). Optimal variable weighting for ultrametric and additive trees and k -means partitioning: Methods and software. J. Classification, 18(2):245-271.
  19. Masthoff, J. (2011). Group recommender systems: Combining individual models. In Recommender Systems Handbook, pages 677-702. Springer.
  20. McCarthy, J. (2002). Pocket RestaurantFinder: A situated recommender system for groups. In Workshop on Mobile Ad-Hoc Communication at the 2002 ACM Conference on Human Factors in Computer Systems.
  21. McCarthy, J. F. and Anagnost, T. D. (1998). Musicfx: An arbiter of group preferences for computer supported collaborative workouts. In CSCW 7898, Proceedings of the ACM 1998 Conference on Computer Supported Cooperative Work, pages 363-372. ACM.
  22. McCarthy, K., Salam ó, M., Coyle, L., McGinty, L., Smyth, B., and Nixon, P. (2006). Cats: A synchronous approach to collaborative group recommendation. In Proceedings of the Nineteenth International Florida Artificial Intelligence Research Society Conference, pages 86-91. AAAI Press.
  23. O'Connor, M., Cosley, D., Konstan, J. A., and Riedl, J. (2001). Polylens: A recommender system for groups of users. In Proceedings of the Seventh European Conference on Computer Supported Cooperative Work, pages 199-218. Kluwer.
  24. Radovanovic, M., Nanopoulos, A., and Ivanovic, M. (2010). Hubs in space: Popular nearest neighbors in high-dimensional data. Journal of Machine Learning Research, 11:2487-2531.
  25. Schafer, J. B., Frankowski, D., Herlocker, J. L., and Sen, S. (2007). Collaborative filtering recommender systems. In The Adaptive Web, Methods and Strategies of Web Personalization, pages 291-324. Springer.
  26. Soete, G. (1988). Ovwtre: A program for optimal variable weighting for ultrametric and additive tree fitting. Journal of Classification, 5(1):101-104.
  27. Zhiwen, Y., Xingshe, Z., and Daqing, Z. (2005). An adaptive in-vehicle multimedia recommender for group users. In Proceedings of the 61st Semiannual Vehicular Technology Conference, volume 5, pages 2800- 2804.
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Paper Citation


in Harvard Style

Boratto L. and Carta S. (2014). Using Collaborative Filtering to Overcome the Curse of Dimensionality when Clustering Users in a Group Recommender System . In Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-758-028-4, pages 564-572. DOI: 10.5220/0004865005640572


in Bibtex Style

@conference{iceis14,
author={Ludovico Boratto and Salvatore Carta},
title={Using Collaborative Filtering to Overcome the Curse of Dimensionality when Clustering Users in a Group Recommender System},
booktitle={Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2014},
pages={564-572},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004865005640572},
isbn={978-989-758-028-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - Using Collaborative Filtering to Overcome the Curse of Dimensionality when Clustering Users in a Group Recommender System
SN - 978-989-758-028-4
AU - Boratto L.
AU - Carta S.
PY - 2014
SP - 564
EP - 572
DO - 10.5220/0004865005640572