ABSTRACT
Collaborative prediction is a powerful technique, useful in domains from recommender systems to guiding the scientific discovery process. Low-rank matrix factorization is one of the most powerful tools for collaborative prediction. This work presents a general approach for active collaborative prediction with the Probabilistic Matrix Factorization model. Using variational approximations or Markov chain Monte Carlo sampling to estimate the posterior distribution over models, we can choose query points to maximize our understanding of the model, to best predict unknown elements of the data matrix, or to find as many "positive" data points as possible. We evaluate our methods on simulated data, and also show their applicability to movie ratings prediction and the discovery of drug-target interactions.
- R. P. Adams, G. E. Dahl, and I. Murray. Incorporating side information in probabilistic matrix factorization with Gaussian processes. 2010.Google Scholar
- C. Boutilier, R. S. Zemel, and B. Marlin. Active collaborative filtering. In UAI. Morgan Kaufmann Publishers Inc, 2002. Google ScholarDigital Library
- P. Dutilleul. The MLE algorithm for the matrix normal distribution. Journal of Statistical Computation and Simulation, 64(2):105--123, 1999.Google ScholarCross Ref
- 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.Google Scholar
- 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.Google Scholar
- R. Garnett, Y. Krishnamurthy, X. Xiong, J. Schneider, and R. Mann. Bayesian optimal active search and surveying. In ICML, 2012.Google Scholar
- M. Gönen, S. A. Khan, and S. Kaski. Kernelized Bayesian matrix factorization. arXiv.org, stat.ML, 2012.Google Scholar
- 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.Google Scholar
- T. Hofmann and J. Puzicha. Latent class models for collaborative filtering. International Joint Conference on Artificial Intelligence, 16:688--693, 1999. Google ScholarDigital Library
- 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.Google ScholarCross Ref
- R. Jin and L. Si. A Bayesian approach toward active learning for collaborative filtering. UAI, pages 278--285, 2004. Google ScholarDigital Library
- 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.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- 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. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- 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.Google Scholar
- R. F. Murphy. An active role for machine learning in drug development. Nature Publishing Group, 7(6):327--330, 2011.Google Scholar
- 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.Google ScholarCross Ref
- 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. Google ScholarDigital Library
- F. Ricci, L. Rokach, B. Shapira, and P. Kantor. Recommender Systems Handbook. Springer, 2011. Google ScholarCross Ref
- I. Rish and G. Tesauro. Active collaborative prediction with maximum margin matrix factorization. Inform. Theory and App. Workshop, 2007.Google Scholar
- 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.Google ScholarCross Ref
- R. Salakhutdinov and A. Mnih. Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. In ICML, pages 880--887, 2008. Google ScholarDigital Library
- R. Salakhutdinov and A. Mnih. Probabilistic matrix factorization. In NIPS, 2008.Google ScholarDigital Library
- H. Shan and A. Banerjee. Generalized probabilistic matrix factorizations for collaborative filtering. In ICDM, pages 1025--1030, 2010. Google ScholarDigital Library
- J. Silva and L. Carin. Active learning for online Bayesian matrix factorization. In KDD, 2012. Google ScholarDigital Library
- N. Srebro, J. Rennie, and T. Jaakkola. Maximum-margin matrix factorization. In NIPS, volume 17, pages 1329--1336, 2005.Google Scholar
- Stan Development Team. Stan: A C library for probability and sampling, version 1.1, 2013.Google Scholar
- S. Tong and D. Koller. Support vector machine active learning with applications to text classification. Journal of Machine Learning Research, 2:45--66, 2002. Google ScholarDigital Library
- X. Yang, H. Steck, Y. Guo, and Y. Liu. On top-k recommendation using social networks. In RecSys '12, 2012. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- 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.Google Scholar
Index Terms
Active learning and search on low-rank matrices
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