AutoML for Deep Recommender Systems: Fundamentals and Advances

Recommender systems have become increasingly important in our daily lives since they play an important role in mitigating the information overload problem, especially in many user-oriented online services. Recommender systems aim to identify a set of items that best match users' explicit or implicit preferences, by utilizing the user and item interactions to improve the accuracy. With the fast advancement of deep neural networks (DNNs) in the past few decades, recommendation techniques have achieved promising performance. However, we still meet three inherent challenges to design deep recommender systems (DRS): 1) the majority of existing DRS are developed based on hand-crafted components, which requires ample expert knowledge recommender systems; 2) human error and bias can lead to suboptimal components, which reduces the recommendation effectiveness; 3) non-trivial time and engineering efforts are usually required to design the task-specific components in different recommendation scenarios. In this tutorial, we aim to give a comprehensive survey on the recent progress of advanced Automated Machine Learning (AutoML) techniques for solving the above problems in deep recommender systems. More specifically, we will present feature selection, feature embedding search, feature interaction search, and whole DRS pipeline model training and comprehensive search for deep recommender systems. In this way, we expect academic researchers and industrial practitioners in related fields can get deep understanding and accurate insight into the spaces, stimulate more ideas and discussions, and promote developments of technologies in recommendations.