  There has been growing interests in recent years from both practical and
research perspectives for session-based recommendation tasks as long-term user
profiles do not often exist in many real-life recommendation applications. In
this case, recommendations for user's immediate next actions need to be
generated based on patterns in anonymous short sessions. An often overlooked
aspect is that new items with limited observations arrive continuously in many
domains (e.g. news and discussion forums). Therefore, recommendations need to
be adaptive to such frequent changes. In this paper, we benchmark a new
nonparametric method called context tree (CT) against various state-of-the-art
methods on extensive datasets for session-based recommendation task. Apart from
the standard static evaluation protocol adopted by previous literatures, we
include an adaptive configuration to mimic the situation when new items with
limited observations arrives continuously. Our results show that CT outperforms
two best-performing approaches (recurrent neural network; heuristic-based
nearest neighbor) in majority of the tested configurations and datasets. We
analyze reasons for this and demonstrate that it is because of the better
adaptation to changes in the domain, as well as the remarkable capability to
learn static sequential patterns. Moreover, our running time analysis
illustrates the efficiency of using CT as other nonparametric methods.
