  User Behavior Modeling (UBM) plays a critical role in user interest learning,
which has been extensively used in recommender systems. Crucial interactive
patterns between users and items have been exploited, which brings compelling
improvements in many recommendation tasks. In this paper, we attempt to provide
a thorough survey of this research topic. We start by reviewing the research
background of UBM. Then, we provide a systematic taxonomy of existing UBM
research works, which can be categorized into four different directions
including Conventional UBM, Long-Sequence UBM, Multi-Type UBM, and UBM with
Side Information. Within each direction, representative models and their
strengths and weaknesses are comprehensively discussed. Besides, we elaborate
on the industrial practices of UBM methods with the hope of providing insights
into the application value of existing UBM solutions. Finally, we summarize the
survey and discuss the future prospects of this field.
