  Session-based recommendation is a problem setting where the task of a
recommender system is to make suitable item suggestions based only on a few
observed user interactions in an ongoing session. The lack of long-term
preference information about individual users in such settings usually results
in a limited level of personalization, where a small set of popular items may
be recommended to many users. This repeated exposure of such a subset of the
items through the recommendations may in turn lead to a reinforcement effect
over time, and to a system which is not able to help users discover new content
anymore to the desirable extent.
  In this work, we investigate such potential longitudinal effects of
session-based recommendations in a simulation-based approach. Specifically, we
analyze to what extent algorithms of different types may lead to concentration
effects over time. Our experiments in the music domain reveal that all
investigated algorithms---both neural and heuristic ones---may lead to lower
item coverage and to a higher concentration on a subset of the items.
Additional simulation experiments however also indicate that relatively simple
re-ranking strategies, e.g., by avoiding too many repeated recommendations in
the music domain, may help to deal with this problem.
