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GameTrail: Probabilistic Lifecycle Process Model for Deep Game Understanding

Published: 21 October 2024 Publication History

Abstract

As the mobile gaming market experiences significant growth, there is a continuous emergence of new gaming products such as premium games and video mini-games. Meeting their marketing needs and supporting their business growth is essential for long-term prosperity. However, compared with extensive studies on user modeling such as LTV prediction, much less attention has been drawn to special gaming products, especially in terms of understanding their lifecycle stages and corresponding demands. Unlike modeling individual users, understanding games is closely tied to user behavior: the lifecycle of a game encompasses the entire process from initial user interaction to churn, and by accurately identifying and tracking the evolution of the game lifecycle can lead to better personal service. This raises the necessity of comprehensively understanding the lifecycle process model of the game. In this paper, we introduce the GameTrail - Probabilistic Lifecycle Process Model, designed to construct the complete lifecycle and stage representation for games and users through long-term repeated interactions. Specifically, we first initiate the complete game lifecycle using a joint probabilistic stochastic process model by defining the lifecycle stages of both games and users as latent variables and learning it via Bayesian Variation Inference. Furthermore, we employ cross attention and online embedding learning to capture the more recent advertising context changes in the in-game stage transitions. Finally, we collect various games' data from public resources to construct an experimental dataset for our experiments. Meanwhile, more comprehensive experiments conducted on real-world gaming industry datasets have showcased the effectiveness of our approach, showing a relative improvement of 48% and 17% on NMSE and NMAE than the live baseline.

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cover image ACM Conferences
CIKM '24: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management
October 2024
5705 pages
ISBN:9798400704369
DOI:10.1145/3627673
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Published: 21 October 2024

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Author Tags

  1. lifecycle stage
  2. online inference
  3. probabilistic model

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  • Research-article

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  • China National Natural Science Foundation
  • the Fundamental Research Funds for the Central Universities

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CIKM '24
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