Localized matrix factorization for recommendation based on matrix Block Diagonal Forms

WWW 2013 - Proceedings of the 22nd International Conference on World Wide Web

Volumn , Issue , 2013, Pages 1511-1520

  Zhang, Yongfeng ^a   Zhang, Min ^a   Liu, Yiqun ^a   Ma, Shaoping ^a   Feng, Shi ^a  

^a TSINGHUA UNIVERSITY   (China)

Author keywords

Block Diagonal Form; Collaborative Filtering; Graph Partitioning; Matrix Factorization

Indexed keywords

COLLABORATIVE FILTERING; FACTORIZATION; SCALABILITY; WORLD WIDE WEB;

BLOCK DIAGONAL; BORDERED BLOCK DIAGONAL FORM; COLLABORATIVE PREDICTIONS; EFFECTIVENESS AND EFFICIENCIES; GRAPH PARTITIONING; LARGE SPARSE MATRIX; MATRIX FACTORIZATIONS; PREDICTION ACCURACY;

MATRIX ALGEBRA;

EID: 84893055784     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2488388.2488520     Document Type: Conference Paper

References (38)

1. H. Abdi and L. J. Williams. Principal component analysis. WIREs Comp Stat, 2:433-459, 2010.
2. C. Alzate and J. A. Suykens. Multiway spectral clustering with out-of-sample extensions through weighted kernel pca. PAMI, 32:335-347, 2010.
3. B. Arsic et al. Graph spectral techniques in computer sciences. Appl. Anal. Discrete Math, 6:1-30, 2012.
4. C. Aykanat et al. Permuting sparse rectangular matrices into block-diagonal form. SISC, 2004.
5. A. Banerjee, S. Merugu, I. S. Dhillon, and J. Ghosh. Clustering with bregman divergences. JMLR, 2005.
6. E. Boman and M. Wolf. A nested dissection approach to sparse matrix partitioning for parallel computations. AMM, 2007.
7. T. N. Bui and C. Jones. Finding good approximate vertex and edge partitions is np-hard. IPL, 1992.
8. J. Chen and Y. Saad. Dense subgraph extraction with application to community detection. TKDE, 2012.
9. I. S. Dhilon et al. Concept decompositions for large sparse text data using clustering. JMLR, 2001.
10. E. Gallopoulos and D. Zeimpekis. Clsi: A exible approximation scheme from clustered term-document matrices. SDM, 2005.
11. R. Gemulla et al. Large-scale matrix factorization with distributed stochastic gradient descent. KDD, 2011.
12. J. Herlocker et al. An algorithmic framework for performing collaborative filtering. SIGIR, 1999.
13. G. Karypis. Metis - A software package for partitioning unstructured graphs, meshes, and computing fill reducing orderings of sparse matrices-v5.0. 2011.
14. G. Karypis et al. A fast and high quality multilevel scheme for partitioning irregular graphs. SISC, 1999.
15. J. Kim, I. Hwang, Y. Kim, et al. Genetic approaches for graph partitioning: A survey. CECCO, 2011.
16. Y. Koren. Factorization meets the neighborhood: a multifaceted collaborative filtering model. KDD, 2008.
17. Y. Koren, R. Bell, et al. Matrix factorization techniques for recommender systems. Computer, 2009.
18. D. Lee and H. Seung. Learning the parts of objects with nonnegative matrix factorization. Nature, 1999.
19. D. Lee and H. Seung. Algorithms for non-negative matrix factorization. NIPS, 2001.
20. C. Liu, H. Yang, J. Fan, L. He, et al. Distributed nonnegative matrix factorization for web-scale dyadic data analysis on mapreduce. WWW, 2010.
21. J. Liu, M. Chen, J. Chen, et al. Recent advances in personal recommender systems. JISS, 2009.
22. M. J. Pazzani and D. Billsus. Content-based recommendation systems. Adaptive Web LNCS, 2007.
23. J. Rennie et al. Fast maximum margin matrix factorization for collaborative prediction. ICML, 2005.
24. P. Resnick et al. Grouplens: An open architecture for collaborative filtering of netnews. CSCW, 1994.
25. R. Salakhutdinov and A. Mnih. Bayesian probabilistic matrix factorization using markov chain monte carlo. ICML, 2008.
26. R. Salakhutdinov and A. Mnih. Probabilistic matrix factorization. NIPS, 2008.
27. P. Sanders and C. Schulz. Engineering multilevel graph partitioning algorithms. ESA, 2011.
28. B. Sarwar et al. Application of dimension reduction in recommender systems - a case study. WebKDD, 2000.
29. B. Sarwar, G. Karypis, et al. Item-based collaborative filtering recommendation algorithms. WWW, 2001.
30. B. Sarwar, G. Karypis, et al. Incremental singular value decomposition algorithms for highly scalable recommender systems. ICCIT, 2002.
31. B. Savas et al. Clustered low rank approximation of graphs in information science applications. SDM, 2011.
32. A. P. Singh and G. J. Gordon. Relational learning via collective matrix factorization. KDD, 2008.
33. N. Srebro and T. Jaakkola. Weighted low-rank approximations. ICML, 2003.
34. N. Srebro, J. Rennie, and T. S. Jaakkola. Maximum-margin matrix factorization. NIPS, 2005.
35. X. Su and T. Khoshgoftaar. A survey of collaborative filtering techniques. Advances in AI., 2009.
36. G. Takacs, I. Pilaszy, B. Nemeth, and D. Tikk. Investigation of various matrix factorization methods for large recommender systems. ICDM, 2008.
37. B. Xu, J. Bu, and C. Chen. An exploration of improving collaborative recommender systems via user-item subgroups. WWW, 2012.
38. G. Xue, C. Lin, Q. Yang, et al. Scalable collaborative filtering using cluster-based smoothing. SIGIR, 2005.