Learning from a large number of feature combinations

Convex Factorization Machines (CFM) are a high-accuracy regression model that can handle a large number of feature combinations. CFM is general-purpose and can be applied to a wide range of tasks: e.g., house price prediction, recommender systems and genome analysis. The proposed method can handle a large number of feature combinations by using a low-rank constraint. Moreover, it is guaranteed to obtain a global optimum. In future work, to further improve predictive accuracy, we plan to support higher-order feature combinations. Besides recommender systems, applications include predicting combinations of genes that are responsible for diseases, which would help find effective cures.

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