Collaborative Deep Forest Learning for Recommender Systems
Soheila Molaei, Amirhossein Havvaei, Hadi Zare, Mahdi Jalili
Abstract
Collaborative filtering (CF) is one of the most practical approaches on recommendation systems by predicting users' preferences for items based on the user-item interaction information. Besides the connections between users and items, social networks among users can provide auxiliary information to improve the performance of recommender systems. Here, we propose an end-to-end deep learning framework by learning latent social features to embed in a CF approach. First, representation learning is employed on the rating matrix to extract the latent social features. Then, a novel deep learning approach based on cascade tree forest is used in the recommendation process. Experiments on real-world datasets from different domains demonstrate that the proposed Collaborative Deep Forest Learning (CDFL) outperforms the state-of-the-art CF recommendation methods.