Collaborative Variational Deep Learning for Healthcare Recommendation

Healthcare recommender system (HRS) has shown the great potential of targeting medical experts or patients, and plays a key role in improving an individual's health by providing insightful recommendations. The HRSs generate recommendations based on a successful and widely applied method known as collaborative filtering (CF). Despite its success, the CF suffers from data sparsity and cold-start problem, which results in the poor quality of recommendations. In particular, it is a great challenge to seeking information relevant to patients' condition, and understanding the medical terms and relationships between them in HRSs. To address these problems, we design a novel collaborative variational deep learning model (CVDL) to exploit multi-sourced information for providing appropriate healthcare recommendations in primary care service. CVDL employs additional variational autoencoder (VAE) to learn deep latent representations for item contents (the description of primary care doctors) in latent space, instead of observation space through an inference network. Meanwhile, the CVDL extracts latent user (patient) features by incorporating user profile in a VAE neural network. Therefore, the CVDL can learn better implicit relationships between items and users from item content, user profile, and rating matrix. In addition, a Stochastic Gradient Variational Bayes (SGVB) approach is proposed to calculate the maximum posterior estimates for learning model parameters. The experiments conducted on three datasets have indicated that our method significantly outperforms the state-of-the-art hybrid CF methods.