Using graph partitioning techniques for neighbour selection in user-based collaborative filtering

RecSys'12 - Proceedings of the 6th ACM Conference on Recommender Systems

Volumn , Issue , 2012, Pages 213-216

  Bellogín, Alejandro ^a   Parapar, Javier ^b  

^a UNIVERSIDAD AUTÓNOMA DE MADRID   (Spain)

^b UNIVERSITY OF A CORUÑA   (Spain)

Author keywords

Clustering; Collaborative filtering; Normalised cut; Recommender systems

Indexed keywords

CLUSTER-BASED; CLUSTER-BASED METHODS; CLUSTERING; COLLABORATIVE FILTERING; COLLABORATIVE FILTERING ALGORITHMS; GRAPH PARTITIONING; NORMALISED CUT; SIMILARITY METRICS; SPECTRAL CLUSTERING;

CLUSTERING ALGORITHMS; CORRELATION METHODS;

RECOMMENDER SYSTEMS;

EID: 84867344192     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2365952.2365997     Document Type: Conference Paper

References (15)

1. ADOMAVICIUS, G., AND TUZHILIN, A. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17, 6 (2005), 734-749. (Pubitemid 40860454)
2. BELLOGÍN, A., CASTELLS, P., AND CANTADOR, I. Precision-oriented evaluation of recommender systems: an algorithmic comparison. In RecSys (2011), pp. 333-336.
3. DESROSIERS, C., AND KARYPIS, G. A comprehensive survey of neighborhood-based recommendation methods. In Recommender Systems Handbook. 2011, pp. 107-144.
4. HERLOCKER, J. L., KONSTAN, J. A., AND RIEDL, J. An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms. Inf. Retr. 5, 4 (2002), 287-310.
5. JIN, R., DING, C. H. Q., AND KANG, F. A probabilistic approach for optimizing spectral clustering. In NIPS (2005).
6. KOREN, Y. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In KDD '08 (2008), ACM, pp. 426-434.
7. MACQUEEN, J. B. Some methods for classification and analysis of multivariate observations. In Proc. of the fifth Berkeley Symposium on Mathematical Statistics and Probability (1967), L. M. L. Cam and J. Neyman, Eds., vol. 1, University of California Press, pp. 281-297.
8. O'CONNOR, M., AND HERLOCKER, J. Clustering items for collaborative filtering. In ACM SIGIR Workshop on Recommender Systems (1999).
9. O'Donovan, J., AND SMYTH, B. Trust in recommender systems. In IUI (2005), pp. 167-174.
10. RESNICK, P., IACOVOU, N., SUCHAK, M., BERGSTROM, P., AND RIEDL, J. Grouplens: An open architecture for collaborative filtering of netnews. In CSCW (1994), pp. 175-186.
11. SARWAR, B., KARYPIS, G., KONSTAN, J., AND RIEDL, J. Item-based collaborative filtering recommendation algorithms. In WWW '01 (2001), ACM, pp. 285-295.
12. SHANI, G., AND GUNAWARDANA, A. Evaluating recommendation systems. In Recommender Systems Handbook. 2011, pp. 257-297.
13. SHI, J., AND MALIK, J. Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22, 8 (2000), 888-905.
14. VON LUXBURG, U. A tutorial on spectral clustering. Tech. Rep. TR-149, Max Planck Institute for Biological Cybernetics, 2006.
15. XUE, G. R., LIN, C., YANG, Q., XI, W., ZENG, H. J., YU, Y., And CHEN, Z. Scalable collaborative filtering using cluster-based smoothing. In SIGIR (2005), ACM, pp. 114-121.