  Recommender systems are tools that support online users by pointing them to
potential items of interest in situations of information overload. In recent
years, the class of session-based recommendation algorithms received more
attention in the research literature. These algorithms base their
recommendations solely on the observed interactions with the user in an ongoing
session and do not require the existence of long-term preference profiles. Most
recently, a number of deep learning based ("neural") approaches to
session-based recommendations were proposed. However, previous research
indicates that today's complex neural recommendation methods are not always
better than comparably simple algorithms in terms of prediction accuracy.
  With this work, our goal is to shed light on the state-of-the-art in the area
of session-based recommendation and on the progress that is made with neural
approaches. For this purpose, we compare twelve algorithmic approaches, among
them six recent neural methods, under identical conditions on various datasets.
We find that the progress in terms of prediction accuracy that is achieved with
neural methods is still limited. In most cases, our experiments show that
simple heuristic methods based on nearest-neighbors schemes are preferable over
conceptually and computationally more complex methods. Observations from a user
study furthermore indicate that recommendations based on heuristic methods were
also well accepted by the study participants. To support future progress and
reproducibility in this area, we publicly share the session-rec evaluation
framework that was used in our research.
