Factorization Machines (FM) are only used in a narrow range of applications and are not part of the standard toolbox of machine learning models. This is a pity, because even though FMs are recognized as being very successful for recommender system type applications they are a general model to deal with sparse and high dimensional features. Our Factorization Machine implementation provides easy access to many solvers and supports regression, classification and ranking tasks. Such an implementation simplifies the use of FM's for a wide field of applications. This implementation has the potential to improve our understanding of the FM model and drive new development.
翻译:集成机(FM)仅用于范围狭窄的应用中,不属于机器学习模型标准工具箱的一部分,这令人遗憾,因为尽管FM被认为对推荐系统型应用程序非常成功,但它们是处理稀有和高维特征的一般模式。我们的集成机实施方便许多解决者,支持回归、分类和排序任务。这种实施简化了对广泛应用领域的调频的使用。这种实施有可能增进我们对调频模型的理解,推动新的开发。