  Online gaming is growing faster than ever before, with increasing challenges
of providing better user experience. 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.
