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Collaborative Deep Forest Learning for Recommender Systems
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AI
DOI：10.1109/ACCESS.2021.3054818.png)
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Abstract
En 中文
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.
Keywords:
Feature extraction
Deep learning
Forestry
Data models
Collaboration
Recommender systems
Predictive models
Recommender systems
social networks
deep learning
collaborative filtering
representational learning