Sequence-aware Recommender Systems

Abstract

The majority of research works in the field of collaborative filtering recommender systems is based on the assumption that the input to the recommendation algorithms is a matrix containing user-item interactions. In reality, however, the input often is a sequence of various types of user-item interactions that are recorded over time and where we can have multiple data points per user-item pair. These sequential logs contain a variety of useful information that can be leveraged in the recommendation process, e.g., to predict the immediate next action of a user or to detect short-term trends in the community. In this tutorial we review what we call sequence-aware recommenders, i.e., approaches that aim to exploit the information in sequential interaction logs for a variety of different purposes. We in particular focus on sequential and session-based recommendation techniques and discuss algorithmic proposals as well as evaluation challenges.