Collaborative Deep Forest Learning for Recommender Systems

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.

Automated summary

The paper proposes a new recommendation approach - Collaborative Deep Forest Learning (CDFL), which aims to improve the performance of recommender systems by learning latent social features and outperforms state-of-the-art CF recommendation methods based on experiments with real-world datasets from different domains.