Graph Embedding Augmented Skill Rating System
نویسندگان
چکیده
This paper presents a framework for learning player embeddings in competitive games and events. Players their win-loss relationships are modeled as skill gap graph, which is an undirected weighted graph. The learned from the graph using random walk-based embedding method can reflect relative levels among players. Embeddings low-dimensional vector representations that be conveniently applied to subsequent tasks while still preserving topological In latter part of this paper, Graphical Elo (GElo) introduced application when rating skills. GElo extension classic system. It constructs based on match histories learns it. Afterward, scores were calculated by adjusted according activeness cosine similarities embeddings. executed offline parallel, it non-intrusive existing systems. Experiments public datasets show makes more reliable evaluation than vanilla Elo. experimental results suggest potential applications
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ژورنال
عنوان ژورنال: IEEE transactions on games
سال: 2022
ISSN: ['2475-1502', '2475-1510']
DOI: https://doi.org/10.1109/tg.2022.3221849