Privacy-Aware Factorization-Based Hybrid Recommendation Method for Healthcare Services

Abstract

With the advancements of the Health 2.0 technology, large-scale healthcare services are available online. Recommender systems for healthcare services have emerged for decision assistance. Most existing collaborative recommendation algorithms only mine global interactions while failing to capture the local different information of users or items. Besides, privacy concern is another significant problem to be considered in recommender systems for healthcare services. In this article, a privacy-aware factorization-based hybrid method is proposed for healthcare service recommendations. For better modeling of user preferences and service features, multiview embeddings of users and healthcare services are learned. Besides, we address the privacy problem by integrating local differential privacy and locality-sensitive hashing techniques into the recommendation model for privacy-aware neighbor searching. The final prediction is made based on a stochastic gradient descent learning-based hybrid collaborative model. Experiments demonstrate the effectiveness of the proposed method in both recommendation performance and privacy concerns.