Year: 2026
Type: article
Abstract: The ability to predict blunders in chess plays a crucial role in improving players’ performance and enabling strategic decision-making. We introduce a novel, scalable, and personalized blunder prediction model for chess. Unlike prior work requiring a separate model per player, our unified architecture learns a collaborative user embedding space, allowing it to generalize weaknesses across players and new users. Our hybrid model, inspired by Deep Factorization Machines (DeepFM), fuses a frozen pre-trained CNN (for board embeddings) with dynamically learned user embeddings to model player-board interactions while still utilizing metadata about the state of the board and the user. We demonstrate that this latent ’blunder profile’ is a significantly more powerful predictor of error than a player’s explicit Elo rating. The system achieves state-of-the-art performance (0.801 AUC) on both immediate and non-immediate blunders, offering an efficient and data-sparse-friendly solution for personalized chess analysis. Ultimately, this approach demonstrates the practical viability of deep personalization in complex strategy games, facilitating highly efficient, user-centric learning environments.
Source: Applied Intelligence
Authors: Yarden Rokach, Bracha Shapira
Institution: Ben-Gurion University of the Negev
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,190
OpenAlex ID: W7128950371