Sequence-aware recommendation.

In recent years, more and more recommendation algorithms have been proposed that are based on time-ordered user interaction logs. Algorithms for session-based recommendation tasks are among the most prominent examples of such approaches. Differently from the more traditional matrix completion algorithms, where for each user-item pair only one interaction (e.g., a rating) is considered, sequence-aware algorithms are typically designed to learn sequential patterns from user behavior data. These patterns can then be used to predict the user's next action within an ongoing session or to detect short-term trends in the community. In this tutorial, we first outline the application areas of sequence-aware recommendation. We then focus on sequential and session-based recommendation techniques and discuss algorithmic proposals as well as evaluation challenges. Finally, the tutorial will be concluded by an hands-on session.