Optimizing Endless Runner Game Player Performance Using a Hybrid GMF-MLP Recommendation System Based on Neural Collaborative Filtering
Keywords:
Endless Runner, General Matrix Factorization , Multi-Layer Perceptron, Neural Collaborative Filtering, Game Recommendation SystemAbstract
Endless Runner games feature exponentially increasing difficulty as distance grows, often causing character failure and player frustration, largely because players struggle to select power-ups suited to their current difficulty context. While prior recommender-system research has mostly focused on purchase prediction for monetization, this study instead builds a personalized item recommendation system aimed at reducing failure and maximizing scores. We propose a hybrid Neural Collaborative Filtering (NCF) architecture combining General Matrix Factorization (GMF), which captures linear preferences, with a Multi-Layer Perceptron (MLP), which models non-linear interactions between playstyle and failure context (cause of death). The model was trained on 10,000 gameplay activity logs containing features such as jump count, obstacles avoided, and death cause. Over 20 training epochs, both training and validation accuracy converged to approximately 0.88–0.90, with a negligible gap between the two curves, indicating minimal overfitting. These results demonstrate that integrating GMF and MLP effectively produces recommendations adaptive to dynamic gameplay conditions.
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[1] M. B. Nendya, L. Husniah, H. Wibowo, S. Mu’min, and I. K. D. Senapartha, “Enhancing Endless Runner Gameplay: Integrating Brainwave-Based Natural Interaction with Rule-Based Management System,” in 2024 2nd International Conference on Technology Innovation and Its Applications (ICTIIA), 2024, pp. 1–5. doi: 10.1109/ICTIIA61827.2024.10761532.
[2] M. S. El-Nasr, H. Desurvire, B. Aghabeigi, and A. Drachen, “Game Analytics for Game User Research, Part 1: A Workshop Review and Case Study,” IEEE Comput. Graph. Appl., vol. 33, no. 2, pp. 6–11, 2013, doi: 10.1109/MCG.2013.26.
[3] N. Shaker et al., “The turing test track of the 2012 Mario AI Championship: Entries and evaluation,” in 2013 IEEE Conference on Computational Inteligence in Games (CIG), 2013, pp. 1–8. doi: 10.1109/CIG.2013.6633634.
[4] Y. S. Marandy, F. Nugroho, and R. Kusumawati, “Jurnal Teknologi dan Manajemen Informatika Ashabul Kahfi Serious Game with Adaptive Recommendation System Based on Knowledge-Based Filtering and MULTIMOORA for Islamic Education,” vol. 11, no. 2, pp. 189–200, 2025.
[5] A. R. Manikanta, B. A. Shaik, Y. Kancharla, R. Gurram, and G. Ramasamy, “AI-Driven Game Mechanics: Implementing Finite State Machines in Pac-Man,” in 2025 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), 2025, pp. 1–6. doi: 10.1109/IATMSI64286.2025.10985319.
[6] Y. Veres, O., Ilchuk, P., Kots, O., Veres, “Video Game Selection Recommendation System,” in 2024 IEEE 19th International Conference on Computer Science and Information Technologies (CSIT), 2024, pp. 1–4. doi: 10.1109/CSIT65290.2024.10982670.
[7] W. E. Lomanto, V. Andrian, S. Achmad, and R. Sutoyo, “Collaborative Filtering for Steam Games Recommendation,” in 2023 5th International Conference on Cybernetics and Intelligent System (ICORIS), 2023, pp. 1–6. doi: 10.1109/ICORIS60118.2023.10352243.
[8] O. Wehbi et al., “Enhancing Mutual Trustworthiness in Federated Learning for Data-Rich Smart Cities,” IEEE Internet Things J., vol. 12, no. 3, pp. 3105–3117, 2025, doi: 10.1109/JIOT.2024.3476950.
[9] S. Saxena and B. Bhushan, “Enhancing Personalization: A Machine Learning Approach to Modern Recommender Systems,” in 2025 3rd International Conference on Disruptive Technologies (ICDT), 2025, pp. 465–470. doi: 10.1109/ICDT63985.2025.10986525.
[10] X. He, Z. He, J. Song, Z. Liu, Y.-G. Jiang, and T.-S. Chua, “NAIS: Neural Attentive Item Similarity Model for Recommendation,” IEEE Trans. Knowl. Data Eng., vol. 30, no. 12, pp. 2354–2366, 2018, doi: 10.1109/TKDE.2018.2831682.
[11] R. Dhayanidhi and N. R. Rajalakshmi, “Comparison of NCF and PMF using Movie Lens dataset,” in 2024 Asian Conference on Intelligent Technologies (ACOIT), 2024, pp. 1–5. doi: 10.1109/ACOIT62457.2024.10939418.
[12] A. Bertens, Paul, Guitart, Anna, Chen, Pei Pei, Perianez, “A Machine-Learning Item Recommendation System for Video Games,” IEEE Conf. Comput. Intell. Games, CIG, vol. 2018-Augus, 2018, doi: 10.1109/CIG.2018.8490456.
[13] H. Lin, M. Bao, and C. Kang, “A Spatiotemporal Deep Learning-Based Multisource Data Analytics Framework for Basketball Game,” IEEE Access, vol. 12, pp. 73066–73078, 2024, doi: 10.1109/ACCESS.2024.3403688.
[14] X. He, L. Liao, H. Zhang, L. Nie, X. Hu, and T. S. Chua, “Neural collaborative filtering,” 26th Int. World Wide Web Conf. WWW 2017, pp. 173–182, 2017, doi: 10.1145/3038912.3052569.
[15] “IEEE Draft Standard for Mobile Gaming Performance Evaluation and Optimization,” 2021
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