DeepHCF: A Deep Learning Based Hybrid Collaborative Filtering Approach for Recommendation Systems

Meshal Alfarhood, Jianlin Cheng

Abstract: The data sparsity is a significant challenge for collaborative filtering methods in recommendation systems for making accurate recommendations. Several approaches have been published to address this issue. Most of them usually use only one source of data to train their model, and other approaches still have lower performance especially when the sparsity of data is very high. In this paper, we use a deep learning based model, DeepHCF, to tackle this problem. DeepHCF uses two sources of data, ratings matrix and item reviews, to train two deep models via joint training. The user-item ratings matrix data is trained using a Multi-Layer Perceptron (MLP), while other side information is trained using a Convolutional Neural Network (CNN). The users and items' latent features learned by each model are utilized by factorization machines for our model prediction. Extensive experimental results on four different realworld datasets show that DeepHCF achieves on average a 7.42% improvement over the second most accurate method, when the dataset has over 99.9% of sparsity.