  Recommender systems help users find relevant items of interest, for example
on e-commerce or media streaming sites. Most academic research is concerned
with approaches that personalize the recommendations according to long-term
user profiles. In many real-world applications, however, such long-term
profiles often do not exist and recommendations therefore have to be made
solely based on the observed behavior of a user during an ongoing session.
Given the high practical relevance of the problem, an increased interest in
this problem can be observed in recent years, leading to a number of proposals
for session-based recommendation algorithms that typically aim to predict the
user's immediate next actions. In this work, we present the results of an
in-depth performance comparison of a number of such algorithms, using a variety
of datasets and evaluation measures. Our comparison includes the most recent
approaches based on recurrent neural networks like GRU4REC, factorized Markov
model approaches such as FISM or FOSSIL, as well as simpler methods based,
e.g., on nearest neighbor schemes. Our experiments reveal that algorithms of
this latter class, despite their sometimes almost trivial nature, often perform
equally well or significantly better than today's more complex approaches based
on deep neural networks. Our results therefore suggest that there is
substantial room for improvement regarding the development of more
sophisticated session-based recommendation algorithms.
