  Because of the large number of online games available nowadays, online game
recommender systems are necessary for users and online game platforms. The
former can discover more potential online games of their interests, and the
latter can attract users to dwell longer in the platform. This paper
investigates the characteristics of user behaviors with respect to the online
games on the Steam platform. Based on the observations, we argue that a
satisfying recommender system for online games is able to characterize:
personalization, game contextualization and social connection. However,
simultaneously solving all is rather challenging for game recommendation.
Firstly, personalization for game recommendation requires the incorporation of
the dwelling time of engaged games, which are ignored in existing methods.
Secondly, game contextualization should reflect the complex and high-order
properties of those relations. Last but not least, it is problematic to use
social connections directly for game recommendations due to the massive noise
within social connections. To this end, we propose a Social-aware
Contextualized Graph Neural Recommender System (SCGRec), which harnesses three
perspectives to improve game recommendation. We conduct a comprehensive
analysis of users' online game behaviors, which motivates the necessity of
handling those three characteristics in the online game recommendation.
