With the ever-growing volume, complexity and dynamicity of online information, recommender system is an effective key solution to overcome such information overload. In recent years, deep learning’s revolutionary advances in speech recognition, image analysis and natural language processing have drawn significant attention. Meanwhile, recent studies also demonstrate its effectiveness in coping with information retrieval and recommendation tasks. Applying deep learning techniques into recommender system has been gaining momentum due to its state-of-the-art performances and high-quality recommendations. In contrast to traditional recommendation models, deep learning provides a better understanding of user’s demands, item’s characteristics and historical interactions between them. This article provides a comprehensive review of recent research efforts on deep learning based recommender systems towards fostering innovations of recommender system research. A taxonomy of deep learning based recommendation models is presented and used to categorise surveyed articles. Open problems are identified based on the insightful analytics of the reviewed works and potential solutions discussed. Deep Learning based Recommender System: A Survey and New Perspectives

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