technical-note

Sequential Recommendation in Online Games with Multiple Sequences, Tasks and User Levels

Authors:

Chen LinAuthors Info & Claims

SSTD '21: Proceedings of the 17th International Symposium on Spatial and Temporal Databases

Pages 130 - 140

https://doi.org/10.1145/3469830.3470906

Published: 23 August 2021 Publication History

Abstract

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 M3Rec) in Tencent Games platform, which can fully utilize complex data in online games. We leverage Graph Neural Network and multi-task learning to design M3Rec in order to model the complex information in the heterogeneous sequential recommendation scenario of Tencent Games. We verify the effectiveness of M3Rec on three online games of Tencent Games platform, in both offline and online evaluations. The results show that M3Rec successfully addresses the challenges of recommendation in online games, and it generates superior recommendations compared with state-of-the-art sequential recommendation approaches.

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Cited By

Jiang SZhang LXu HHuang JHe QZhou XHuang LJiang JSerra ESpezzano F(2024)GameTrail: Probabilistic Lifecycle Process Model for Deep Game UnderstandingProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679736(994-1003)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679736
Pei BLiu DChen J(2024)Game Recommendation Based on Reviews and Neighbor Interaction with Generative Adversarial Networks2024 16th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI)10.1109/IIAI-AAI63651.2024.00072(357-362)Online publication date: 6-Jul-2024
https://doi.org/10.1109/IIAI-AAI63651.2024.00072
Yun JKwak WKim JSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Multi Datasource LTV User Representation (MDLUR)Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599871(5500-5508)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599871

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Sequential Recommendation in Online Games with Multiple Sequences, Tasks and User Levels
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SSTD '21: Proceedings of the 17th International Symposium on Spatial and Temporal Databases

August 2021

173 pages

ISBN:9781450384254

DOI:10.1145/3469830

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Published: 23 August 2021

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Natural Science Foundation of Fujian Province China
Natural Science Foundation of China
Joint Innovation Research Program of Fujian Province China

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SSTD '21

SSTD '21: 17th International Symposium on Spatial and Temporal Databases

August 23 - 25, 2021

virtual, USA

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Cited By

Jiang SZhang LXu HHuang JHe QZhou XHuang LJiang JSerra ESpezzano F(2024)GameTrail: Probabilistic Lifecycle Process Model for Deep Game UnderstandingProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679736(994-1003)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679736
Pei BLiu DChen J(2024)Game Recommendation Based on Reviews and Neighbor Interaction with Generative Adversarial Networks2024 16th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI)10.1109/IIAI-AAI63651.2024.00072(357-362)Online publication date: 6-Jul-2024
https://doi.org/10.1109/IIAI-AAI63651.2024.00072
Yun JKwak WKim JSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Multi Datasource LTV User Representation (MDLUR)Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599871(5500-5508)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599871

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