jmlr jmlr2012 jmlr2012-88 jmlr2012-88-reference knowledge-graph by maker-knowledge-mining

88 jmlr-2012-PREA: Personalized Recommendation Algorithms Toolkit


Source: pdf

Author: Joonseok Lee, Mingxuan Sun, Guy Lebanon

Abstract: Recommendation systems are important business applications with significant economic impact. In recent years, a large number of algorithms have been proposed for recommendation systems. In this paper, we describe an open-source toolkit implementing many recommendation algorithms as well as popular evaluation metrics. In contrast to other packages, our toolkit implements recent state-of-the-art algorithms as well as most classic algorithms. Keywords: recommender systems, collaborative filtering, evaluation metrics


reference text

J. Breese, D. Heckerman, and C. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In Proc. of Uncertainty in Artificial Intelligence, 1998. A. Gunawardana and G. Shani. A survey of accuracy evaluation metrics of recommendation tasks. Journal of Machine Learning Research, 10:2935–2962, 2009. N. D. Lawrence and R. Urtasun. Non-linear matrix factorization with gaussian processes. In Proc. of the International Conference of Machine Learning, 2009. D. Lee and H. Seung. Algorithms for non-negative matrix factorization. In Advances in Neural Information Processing Systems, 2001. J. Lee, M. Sun, S. Kim, and G. Lebanon. Automatic feature induction for stagewise collaborative filtering. In Advances in Neural Information Processing Systems, 2012a. J. Lee, M. Sun, and G. Lebanon. A comparative study of collaborative filtering algorithms. ArXiv Report 1205.3193, 2012b. D. Lemire and A. Maclachlan. Slope one predictors for online rating-based collaborative filtering. Society for Industrial Mathematics, 5:471–480, 2005. A. Paterek. Improving regularized singular value decomposition for collaborative filtering. Statistics, 2007:2–5, 2007. R. Salakhutdinov and A. Mnih. Probabilistic matrix factorization. In Advances in Neural Information Processing Systems, 2008a. R. Salakhutdinov and A. Mnih. Bayesian probabilistic matrix factorization using markov chain monte carlo. In Proc. of the International Conference on Machine Learning, 2008b. B. Sarwar, G. Karypis, J. Konstan, and J. Reidl. Item-based collaborative filtering recommendation algorithms. In Proc. of the International Conference on World Wide Web, 2001. X. Su and T. M. Khoshgoftaar. A survey of collaborative filtering techniques. Advances in Artificial Intelligence, 2009:4:2–4:2, January 2009. M. Sun, G. Lebanon, and P. Kidwell. Estimating probabilities in recommendation systems. In Proc. of the International Conference on Artificial Intelligence and Statistics, 2011. M. Sun, G. Lebanon, and P. Kidwell. Estimating probabilities in recommendation systems. Journal of the Royal Statistical Society, Series C, 61(3):471–492, 2012. K. Yu, S. Zhu, J. Lafferty, and Y. Gong. Fast nonparametric matrix factorization for large-scale collaborative filtering. In Proc. of ACM SIGIR Conference, 2009. 2703