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 ...

Feature-based collaborative filtering models, such as state-of-the-art factorization machines and regression-based latent factor models, rarely consider features' structural information, ignoring the heterogeneity of inter-type and intra-type relationships. Naïvely treating all feature pairs equally would potentially deteriorate the overall recommendation performance. In addition, human prior knowledge and other hierarchical or graphical ...

Content recommendation systems are typically based on one of the following paradigms: user based customization, or recommendations based on either collaborative filtering or low rank matrix factorization methods, or with systems that impute user interest profiles based on content browsing behavior and retrieve items similar to the interest profiles. All ...

Over the past two decades, a large amount of research effort has been devoted to developing algorithms that generate recommendations. The resulting research progress has established the importance of the user-item (U-I) matrix, which encodes the individual preferences of users for items in a collection, for recommender systems. The U-I ...