  Recommender systems (RSs) have been playing an increasingly important role
for informed consumption, services, and decision-making in the overloaded
information era and digitized economy. In recent years, session-based
recommender systems (SBRSs) have emerged as a new paradigm of RSs. Different
from other RSs such as content-based RSs and collaborative filtering-based RSs
which usually model long-term yet static user preferences, SBRSs aim to capture
short-term but dynamic user preferences to provide more timely and accurate
recommendations sensitive to the evolution of their session contexts. Although
SBRSs have been intensively studied, neither unified problem statements for
SBRSs nor in-depth elaboration of SBRS characteristics and challenges are
available. It is also unclear to what extent SBRS challenges have been
addressed and what the overall research landscape of SBRSs is. This
comprehensive review of SBRSs addresses the above aspects by exploring in depth
the SBRS entities (e.g., sessions), behaviours (e.g., users' clicks on items)
and their properties (e.g., session length). We propose a general problem
statement of SBRSs, summarize the diversified data characteristics and
challenges of SBRSs, and define a taxonomy to categorize the representative
SBRS research. Finally, we discuss new research opportunities in this exciting
and vibrant area.
