A Library of Factorization Machines in R Based on libfm
supports both ℓ1 and ℓ2 regularized Factorization Machines(FM)
provides some optimization routines such as SGD, FTRL-Proximal, TDAP, ALS as well as MCMC for Bayesian inference
Steffen Rendle (2012): Factorization Machines with libFM, in ACM Trans. Intell. Syst. Technol., 3(3), May
Steffen Rendle (2010): Factorization Machines, in Proceedings of the 10th IEEE International Conference on Data Mining (ICDM 2010), Sydney, Australia.
Steffen Rendle, Zeno Gantner, Christoph Freudenthaler, Lars Schmidt-Thieme (2011): Fast Context-aware Recommendations with Factorization Machines, in Proceeding of the 34th international ACM SIGIR conference on Research and development in information retrieval (SIGIR 2011), Beijing, China.
Christoph Freudenthaler, Lars Schmidt-Thieme, Steffen Rendle (2011): Bayesian Factorization Machines, in Workshop on Sparse Representation and Low-rank Approximation, Neural Information Processing Systems (NIPS-WS), Granada, Spain.
Steffen Rendle (2012): Learning Recommender Systems with Adaptive Regularization, in Proceedings of the 5th ACM International Conference on Web Search and Data Mining (WSDM 2012), Seattle.
Steffen Rendle (2013): Scaling Factorization Machines to Relational Data, in Proceedings of the 39th international conference on Very Large Data Bases (VLDB 2013), Trento, Italy.
Mcmahan, H. B., Holt, G., Sculley, D., Young, M., Ebner, D., & Grady, J., et al. (2013). Ad click prediction: a view from the trenches. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Vol.7, pp.76-77). ACM.
Tsuruoka, Y., Tsujii, J., & Ananiadou, S. (2009). Stochastic gradient descent training for L1-regularized log-linear models with cumulative penalty. ACL 2009, Proceedings of the, Meeting of the Association for Computational Linguistics and the, International Joint Conference on Natural Language Processing of the Afnlp, 2-7 August 2009, Singapore (pp.477-485).