/CisRec

Cis Recommender

Primary LanguageJava

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)


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Dependence: colt (http://acs.lbl.gov/software/colt/)