  The session-based recommendation (SBR) garners increasing attention due to
its ability to predict anonymous user intents within limited interactions.
Emerging efforts incorporate various kinds of side information into their
methods for enhancing task performance. In this survey, we thoroughly review
the side information-driven session-based recommendation from a data-centric
perspective. Our survey commences with an illustration of the motivation and
necessity behind this research topic. This is followed by a detailed
exploration of various benchmarks rich in side information, pivotal for
advancing research in this field. Moreover, we delve into how these diverse
types of side information enhance SBR, underscoring their characteristics and
utility. A systematic review of research progress is then presented, offering
an analysis of the most recent and representative developments within this
topic. Finally, we present the future prospects of this vibrant topic.
