Active learning and search on low-rank matrices

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn Part F128815, Issue , 2013, Pages 212-220

  Sutherland, Dougal J ^a   Póczos, Barnabás ^a   Schneider, Jeff ^a  

^a Carnegie Mellon University ^*  (United States)

Author keywords

Active learning; Active search; Coldstart; Collaborative filtering; Drug discovery; Matrix factorization; Recommender systems

Indexed keywords

ARTIFICIAL INTELLIGENCE; COLLABORATIVE FILTERING; DATA MINING; EDUCATION; FACTORIZATION; FORECASTING; MARKOV PROCESSES; MONTE CARLO METHODS; QUERY PROCESSING; RECOMMENDER SYSTEMS;

ACTIVE LEARNING; ACTIVE SEARCHES; COLD-START; DRUG DISCOVERY; MATRIX FACTORIZATIONS;

MATRIX ALGEBRA;

EID: 84977886950     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2487575.2487627     Document Type: Conference Paper

References (32)

1. R. P. Adams, G. E. Dahl, and I. Murray. Incorporating side information in probabilistic matrix factorization with Gaussian processes. 2010.
2. C. Boutilier, R. S. Zemel, and B. Marlin. Active collaborative filtering. In UAI. Morgan Kaufmann Publishers Inc, 2002.
3. P. Dutilleul. The MLE algorithm for the matrix normal distribution. Journal of Statistical Computation and Simulation, 64(2):105-123, 1999.
4. A. Eriksson and A. Van Den Hengel. Efficient computation of robust low-rank matrix approximations in the presence of missing data using the L1 norm. CVPR, pages 771-778, 2010.
5. R. Garnett, Y. Krishnamurthy, D. Wang, J. Schneider, and R. Mann. Bayesian Optimal Active Search on Graphs. In Ninth Workshop on Mining and Learning with Graphs, 2011.
6. R. Garnett, Y. Krishnamurthy, X. Xiong, J. Schneider, and R. Mann. Bayesian optimal active search and surveying. In ICML, 2012.
7. M. Gönen, S. A. Khan, and S. Kaski. Kernelized Bayesian matrix factorization. arXiv.org, stat.ML, 2012.
8. M. D. Hoffman and A. Gelman. The no-U-Turn sampler: Adaptively setting path lengths in Hamiltonian Monte Carlo. Journal of Machine Learning Research, In press.
9. T. Hofmann and J. Puzicha. Latent class models for collaborative filtering. International Joint Conference on Artificial Intelligence, 16:688-693, 1999.
10. L. Isserlis. On a formula for the product-moment coefficient of any order of a normal frequency distribution in any number of variables. Biometrika, 12:134-139, 1918.
11. R. Jin and L. Si. A Bayesian approach toward active learning for collaborative filtering. UAI, pages 278-285, 2004.
12. R. Karimi, C. Freudenthaler, A. Nanopoulos, and L. Schmidt-Thieme. Active learning for aspect model in recommender systems. IEEE Symposium on Computational Intelligence and Data Mining (CIDM), pages 162-167, 2011.
13. R. Karimi, C. Freudenthaler, A. Nanopoulos, and L. Schmidt-Thieme. Non-myopic active learning for recommender systems based on matrix factorization. Information Reuse and Integration (IRI), pages 299-303, 2011.
14. R. Karimi, C. Freudenthaler, A. Nanopoulos, and L. Schmidt-Thieme. Towards optimal active learning for matrix factorization in recommender systems. In Tools with Artificial Intelligence (ICTAI), pages 1069-1076, 2011.
15. R. Karimi, C. Freudenthaler, A. Nanopoulos, and L. Schmidt-Thieme. Exploiting the characteristics of matrix factorization for active learning in recommender systems. In RecSys'12, 2012.
16. C. Knox, V. Law, T. Jewison, P. Liu, S. Ly, A. Frolkis, A. Pon, K. Banco, C. Mak, V. Neveu, Y. Djoumbou, R. Eisner, A. C. Guo, and D. S. Wishart. DrugBank 3.0: A comprehensive resource for'omics' research on drugs. Nucleic Acids Research, 39(Database):D1035-D1041, 2010.
17. R. F. Murphy. An active role for machine learning in drug development. Nature Publishing Group, 7(6):327-330, 2011.
18. R. M. Neal. MCMC using Hamiltonian dynamics. In S. Brooks, A. Gelman, G. L. Jones, and X.-L. Meng, editors, Handbook of Markov Chain Monte Carlo, Handbooks of Modern Statistical Methods. Chapman & Hall/CRC, 2011.
19. J. Rennie and N. Srebro. Fast maximum margin matrix factorization for collaborative prediction. In Proceedings of the 22nd International Conference on Machine Learning, pages 713-719. 2005.
20. F. Ricci, L. Rokach, B. Shapira, and P. Kantor. Recommender Systems Handbook. Springer, 2011.
21. I. Rish and G. Tesauro. Active collaborative prediction with maximum margin matrix factorization. Inform. Theory and App. Workshop, 2007.
22. N. Rubens, D. Kaplan, and M. Sugiyama. Active learning in recommender systems. In P. Kantor, F. Ricci, L. Rokach, and B. Shapira, editors, Recommender Systems Handbook, pages 735-767. Springer, 2011.
23. R. Salakhutdinov and A. Mnih. Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. In ICML, pages 880-887, 2008.
24. R. Salakhutdinov and A. Mnih. Probabilistic matrix factorization. In NIPS, 2008.
25. H. Shan and A. Banerjee. Generalized probabilistic matrix factorizations for collaborative filtering. In ICDM, pages 1025-1030, 2010.
26. J. Silva and L. Carin. Active learning for online Bayesian matrix factorization. In KDD, 2012.
27. N. Srebro, J. Rennie, and T. Jaakkola. Maximum-margin matrix factorization. In NIPS, volume 17, pages 1329-1336, 2005.
28. Stan Development Team. Stan: A C++ library for probability and sampling, version 1.1, 2013.
29. S. Tong and D. Koller. Support vector machine active learning with applications to text classification. Journal of Machine Learning Research, 2:45-66, 2002.
30. X. Yang, H. Steck, Y. Guo, and Y. Liu. On top-k recommendation using social networks. In RecSys'12, 2012.
31. K. Yu, A. Schwaighofer, and V. Tresp. Collaborative ensemble learning: Combining collaborative and content-based information filtering via hierarchical Bayes. UAI, pages 616-623, 2002.
32. T. Zhou, H. Shan, A. Banerjee, and G. Sapiro. Kernelized probabilistic matrix factorization: Exploiting graphs and side information. In SIAM Data Mining, pages 403-414, 2012.