Deep recommender systems have become increasingly popular in recent years, and have been utilized in a variety of domains, including movies, music, books, search queries,
and social networks. They assist users in their information-seeking tasks by suggesting items (products, services, or information) that best fit their needs and preferences.
Most existing recommender systems are based on static recommendation policies and hand-crafted architectures. Specifically, (i) most recommender systems consider the recommendation
procedure as a static process, which may fail given the dynamic nature of the users' preferences; (ii) existing recommendation policies aim to maximize the immediate reward from
users, while completely overlooking their long-term impacts on user experience; (iii) designing architectures manually requires ample expert knowledge, non-trivial time and
engineering efforts, while sometimes human error and bias can lead to suboptimal architectures. I will introduce my efforts in tackling these challenges via reinforcement
learning (RL) and automated machine learning (AutoML), which can (i) adaptively update the recommendation policies, (ii) optimize the long-term user experience, and (iii)
automatically design the deep architectures for recommender systems.