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Matrix- and Tensor Factorization for Game Content Recommendation

Abstract

Commercial success of modern freemium games hinges on player satisfaction and retention. This calls for the customization of game content or game mechanics in order to keep players engaged. However, whereas game content is already frequently generated using procedural content generation, methods that can reliably assess what kind of content suits a player’s skills or preferences are still few and far between. Addressing this challenge, we propose novel recommender systems based on latent factor models that allow for recommending quests in a single player role-playing game. In particular, we introduce a tensor factorization algorithm to decompose collections of bipartite matrices which represent how players’ interests and behaviors change over time. Extensive online bucket type tests during the ongoing operation of a commercial game reveal that our system is able to recommend more engaging quests and to retain more players than previous handcrafted or collaborative filtering approaches.

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Notes

  1. 1.

    The game is an HTML5 application hosted on Toggo: http://www.toggo.de/serien/trolljaeger/index-4310.htm

  2. 2.

    The QR decomposition of an arbitrary matrix \(\varvec{M}\) is to compute \(\varvec{Q},\varvec{R} \leftarrow QR\bigl ( \varvec{M} \bigr )\), where \(\varvec{Q}\) is orthogonal and \(\varvec{R}\) is upper triangular.

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Acknowledgements

We would like to thank the anonymous reviewers for their insightful comments. We would like to thank Flying Sheep Studios and the developers of Trolljäger: Abenteuer in den Trollhöhlen for creating the platform, providing us with access to their analytics suite, and supporting us with the evaluation process. Additionally, we would like to thank SRTL for supporting us to conduct this study. In parts, the work reported here was funded by the Fraunhofer Center for Machine Learning within the Fraunhofer Cluster of Excellence Cognitive Internet Technologies (CCIT).

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Correspondence to Christian Bauckhage.

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Sifa, R., Yawar, R., Ramamurthy, R. et al. Matrix- and Tensor Factorization for Game Content Recommendation. Künstl Intell (2019). https://doi.org/10.1007/s13218-019-00620-2

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Keywords

  • Player retention
  • Recommender systems
  • Latent factor models