research-article

GMTL: A GART Based Multi-task Learning Model for Multi-Social-Temporal Prediction in Online Games

Authors:

Sha ZhaoAuthors Info & Claims

CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management

Pages 2841 - 2849

https://doi.org/10.1145/3357384.3357830

Published: 03 November 2019 Publication History

Abstract

Multi-social-temporal (MST) data, which represent multi-attributed time series corresponding to the entities in multi-relational social network series, are ubiquitous in real-world and virtual-world dynamic systems, such as online games. Predictions over MST data such as social time series prediction and temporal link weight prediction are of great importance but challenging. They are affected by many complex factors, including temporal characteristics, social characteristics, collaborative characteristics, task characteristics and the intrinsic causality between them. In this paper, we propose a graph attention recurrent network (GART) based multi-task learning model (GMTL) to fuse information across multiple social-temporal prediction tasks. Experiments on an MMORPG dataset demonstrate that GMTL outperforms the state-of-the-art baselines and can significantly improve performances of specific social-temporal prediction task with additional information from others. Our work has been deployed to several MMORPGs in practice and can also expand to many related multi-social-temporal prediction tasks in real-world applications. Case studies on applications for multi-social-temporal prediction show that GMTL produces great value in the actual business in NetEase Games.

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Cited By

Zhao SWu RTao JQu MZhao MFan CZhao H(2023)perCLTV: A General System for Personalized Customer Lifetime Value Prediction in Online GamesACM Transactions on Information Systems10.1145/353001241:1(1-29)Online publication date: 9-Jan-2023
https://dl.acm.org/doi/10.1145/3530012
Wen XZhao SWang HWu RQu MHu TChen GTao JFan C(2023)Multi-Source Multi-Label Learning for User Profiling in Online GamesIEEE Transactions on Multimedia10.1109/TMM.2022.317168325(4135-4147)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/TMM.2022.3171683
Wu RDeng HTao JFan CLiu QChen Ld'Aquin MDietze SHauff CCurry ECudre Mauroux P(2020)Deep Behavior Tracing with Multi-level Temporality Preserved EmbeddingProceedings of the 29th ACM International Conference on Information & Knowledge Management10.1145/3340531.3412696(2813-2820)Online publication date: 19-Oct-2020
https://dl.acm.org/doi/10.1145/3340531.3412696

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Published In

cover image ACM Conferences

CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management

November 2019

3373 pages

ISBN:9781450369763

DOI:10.1145/3357384

General Chairs:
Wenwu Zhu
Tsinghua University, China
,
Dacheng Tao
University of Massachusetts, USA
,
Xueqi Cheng
Institute of Computing Technology, CAS, China
,
Program Chairs:
Peng Cui
Tsinghua University, China
,
Elke Rundensteiner
Worcester Polytechnic Institute, USA
,
David Carmel
Amazon Research, USA
,
Qi He
LinkedIn, USA
,
Jeffrey Xu Yu
Chinese University of Hong Kong, China

Copyright © 2019 ACM.

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Published: 03 November 2019

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November 3 - 7, 2019

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Zhao SWu RTao JQu MZhao MFan CZhao H(2023)perCLTV: A General System for Personalized Customer Lifetime Value Prediction in Online GamesACM Transactions on Information Systems10.1145/353001241:1(1-29)Online publication date: 9-Jan-2023
https://dl.acm.org/doi/10.1145/3530012
Wen XZhao SWang HWu RQu MHu TChen GTao JFan C(2023)Multi-Source Multi-Label Learning for User Profiling in Online GamesIEEE Transactions on Multimedia10.1109/TMM.2022.317168325(4135-4147)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/TMM.2022.3171683
Wu RDeng HTao JFan CLiu QChen Ld'Aquin MDietze SHauff CCurry ECudre Mauroux P(2020)Deep Behavior Tracing with Multi-level Temporality Preserved EmbeddingProceedings of the 29th ACM International Conference on Information & Knowledge Management10.1145/3340531.3412696(2813-2820)Online publication date: 19-Oct-2020
https://dl.acm.org/doi/10.1145/3340531.3412696

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