Recommender systems are intelligent information retrieval applications, and have been leveraged in numerous domains such as e-commerce, movies, music, books, and point-of-interests. They play a crucial role in the users' information-seeking process, and overcome the information overload issue by recommending personalized items (products, services, or information) that best match users' needs and preferences. Driven by the recent advances in machine learning theories and the prevalence of deep learning techniques, there have been tremendous interests in developing deep learning based recommender systems. They have unprecedentedly advanced effectiveness of mining the non-linear user-item relationships and learning the feature representations from massive datasets, which produce great vitality and improvements in recommendations from both academic and industry communities.Despite above prominence of existing deep recommender systems, their adaptiveness and automation still remain under-explored. Thus, in this dissertation, we study the problem of adaptive and automated deep recommender systems. Specifically, we present our efforts devoted to building adaptive deep recommender systems to continuously update recommendation strategies according to the dynamic nature of user preference, which maximizes the cumulative reward from users in the practical streaming recommendation scenarios. In addition, we propose a group of automated and systematic approaches that design deep recommender system frameworks effectively and efficiently from a data-driven manner. More importantly, we apply our proposed models into a variety of real-world recommendation platforms and have achieved promising enhancements of social and economic benefits.
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