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Latent factor models and decision tree based models are widely used in tasks of prediction, ranking and recommendation. Latent factor models have the advantage of interpreting categorical features by a low-dimensional representation, while such an interpretation does not naturally fit numerical features. In contrast, decision tree based models enjoy the advantage of capturing the nonlinear interactions of numerical features, while their capability of handling categorical features is limited by
doi:10.1145/3038912.3052668
dblp:conf/www/ZhaoSH17
fatcat:7khc2ozfofeudaock5bhhscxhi