  Online gaming is a multi-billion-dollar industry, which is growing faster
than ever before. Recommender systems (RS) for online games face unique
challenges since they must fulfill players' distinct desires, at different user
levels, based on their action sequences of various action types. Although many
sequential RS already exist, they are mainly single-sequence, single-task, and
single-user-level. In this paper, we introduce a new sequential recommendation
model for multiple sequences, multiple tasks, and multiple user levels
(abbreviated as M$^3$Rec) in Tencent Games platform, which can fully utilize
complex data in online games. We leverage Graph Neural Network and multi-task
learning to design M$^3$Rec in order to model the complex information in the
heterogeneous sequential recommendation scenario of Tencent Games. We verify
the effectiveness of M$^3$Rec on three online games of Tencent Games platform,
in both offline and online evaluations. The results show that M$^3$Rec
successfully addresses the challenges of recommendation in online games, and it
generates superior recommendations compared with state-of-the-art sequential
recommendation approaches.
