A model unifying matrix factorisation algorithms with kernel machines to do SVM classification under sparsity.
Doesn’t everything immaterial suddenly sound modernistically sexy when you put “machine” on it? “Boltzmann machine”, “soft machine”, “machine for living”…?
Anyway, general factorisations for classificaton problems over categorical data, using some tricks I am not familiar with. See the quora intro.
I don’t need this technology, it turns out, so there is unlikley to me more information here.
Straight outta Konstanz, libfm.
Factorization machines (FM) are a generic approach that allows to mimic most factorization models by feature engineering. This way, factorization machines combine the generality of feature engineering with the superiority of factorization models in estimating interactions between categorical variables of large domain.
- Freudenthaler, C., Schmidt-thieme, L., & Rendle, S. (n.d.) Bayesian Factorization Machines.
- Rendle, S. (2010) Factorization Machines. In 2010 IEEE 10th International Conference on Data Mining (ICDM) (pp. 995–1000). DOI.
- Rendle, S. (2012) Factorization machines with libfm. ACM Transactions on Intelligent Systems and Technology (TIST), 3(3), 57.
- Rendle, S., Gantner, Z., Freudenthaler, C., & Schmidt-Thieme, L. (2011) Fast context-aware recommendations with factorization machines. In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval (pp. 635–644). ACM
- Silbermann, T. (n.d.) libFM & Factorization Machines.