+ abstract = {This study presents a few-shot embedding learning approach to predict the behavior of individual chess players, based on only 100 games. Traditional models have relied on extensive datasets, often requiring thousands of games to achieve accurate move predictions. In contrast, our method leverages a limited number of games to generate dense vector representations, or embeddings, that capture a player's unique style. We trained a neural network to create these embeddings and used them to predict subsequent moves. Our results indicate that the embedding model performs well across various player sets and can accurately identify players even at scale among a great player population, picking out players with 84\% accuracy from among 100k candidates. There are indications that including information on the clock situation during the game improves the embedding process, although our findings are inconclusive. Despite these limitations, our approach shows promise in making personalized chess training more accessible and highlights the potential for embedding learning in human-centered AI applications. Future work will aim to refine both the embedding and move prediction models and explore its application in other domains.},
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