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Cis Recommender
This project forked from zhangsi/cisrec
Cis Recommender
CisRec is a free recommender system toolkit for rating prediction task such as netflix prize. The implemented algorithms include: (1) Probabilistic Matrix Factorization Salakhutdinov, R., & Mnih, A. (2008). Probabilistic matrix factorization. Advances in Neural Information Processing Systems 20. Cambridge, MA: MIT Press http://www.cs.utoronto.ca/~amnih/papers/pmf.pdf (2) SVD++ Yehuda Koren., Factorization meets the neighborhood: A multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'08) (2008), 426¨C434. http://public.research.att.com/~volinsky/netflix/kdd08koren.pdf (3) Restricted Boltzmann Machines Salakhutdinov, R., Mnih, A. Hinton, G, Restricted BoltzmanMachines for Collaborative Filtering, To appear inProceedings of the 24thInternational Conference onMachine Learning 2007. http://www.cs.toronto.edu/~rsalakhu/papers/rbmcf.pdf (4) Probabilistic Latent Semantic Analysis T. Hofmann, Latent Semantic Models for Collaborative Filtering, ACM Transactions on Information Systems 22 (2004), 89C115. http://comminfo.rutgers.edu/~muresan/IR/Docs/Articles/toisHofmann2004.pdf (5) Alternating Least Squares Yunhong Zhou, Dennis Wilkinson, Robert Schreiber and Rong Pan. Large-Scale Parallel Collaborative Filtering for the Netflix Prize. Proceedings of the 4th international conference on Algorithmic Aspects in Information and Management. Shanghai, China pp. 337-348, 2008. http://www.hpl.hp.com/personal/Robert_Schreiber/papers/2008%20AAIM%20Netflix/netflix_aaim08(submitted).pdf (6) M. Jamali and M. Ester, A Matrix Factorization Technique with Trust Propagation for Recommendation in Social Networks, in ACM Conference on Recommender Systems (RecSys'10), Barcelona, Spain, September 2010. http://dl.acm.org/citation.cfm?id=1864736 others: biased baseline, global average, user average, item average and the variants of (1)(2)(3)(4) ======================================================== Dependence: colt (http://acs.lbl.gov/software/colt/)
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