CognitionNet: A Collaborative Neural Network for Play Style Discovery in Online Skill Gaming Platform

被引:1
作者
Talwadker, Rukma [1 ]
Chakrabarty, Surajit [1 ]
Pareek, Aditya [1 ]
Mukherjee, Tridib [1 ]
Saini, Deepak [2 ]
机构
[1] Games24x7, Artificial Intelligence & Sci, Bangalore, Karnataka, India
[2] Games24x7, Prod Delight, Bangalore, Karnataka, India
来源
PROCEEDINGS OF THE 28TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, KDD 2022 | 2022年
关键词
deep learning; representation learning; time series modelling; clustering; psychology understanding;
D O I
10.1145/3534678.3539179
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Games are one of the safest source of realizing self-esteem and relaxation at the same time [29, 40]. An online gaming platform typically has massive data coming in, e.g., in-game actions, player moves, clickstreams, transactions etc. It is rather interesting, as something as simple as data on gaming moves can help create a psychological imprint of the user at that moment, based on her impulsive reactions and response to a situation in the game. Mining this knowledge can: (a) immediately help better explain observed and predicted player behavior; and (b) consequently propel deeper understanding towards players' experience, growth and protection. To this effect, we focus on discovery of the "game behaviours" as micro-patterns formed by continuous sequence of games and the persistent "play styles" of the players' as a sequence of such sequences on an online skill gaming platform for Rummy. The complex sequences of intricate sequences is analysed through a novel collaborative two stage deep neural network, CognitionNet. The first stage focuses on mining game behaviours as cluster representations in a latent space while the second aggregates over these micro patterns (e.g., transitions across patterns) to discover play styles via a supervised classification objective around player engagement. The dual objective allows CognitionNet to reveal several player psychology inspired decision making and tactics. To our knowledge, this is the first and one-of-its-kind research to fully automate the discovery of: (i) player psychology and game tactics from telemetry data; and (ii) relevant diagnostic explanations to players' engagement predictions. The collaborative training of the two networks with differential input dimensions is enabled using a novel formulation of "bridge loss". The network plays pivotal role in obtaining homogeneous and consistent play style definitions and significantly outperforms the SOTA baselines wherever applicable.
引用
收藏
页码:3961 / 3969
页数:9
相关论文
共 43 条
  • [1] Akhter Syed, 2017, THESIS, DOI [10.13140/RG.2.2.22661.19685, DOI 10.13140/RG.2.2.22661.19685]
  • [2] [Anonymous], 2014, P 27 INT C NEUR INF, V2, P2672
  • [3] Braverman Julia, 2010, EJPH
  • [4] Cai Zongwu, 2000, FUNCTIONAL COEFFICIE
  • [5] CAILLOIS Roger., 1962, MAN PLAY GAMES
  • [6] Canossa Alessandro, 2009, PLAY PERSONAS BEHAV
  • [7] Caruana R., 1993, ICML, P41, DOI 10.1016/b978-1-55860-307-3.50012-5
  • [8] Devlinand Jacob, 2019, BERT PRETRAINING DEE
  • [9] Gow Alan, 2005, PERSONALITY INDIVIDU
  • [10] Guo Chonghui, WICOM 2008