This paper presents a framework for learning player embeddings in competitive games and events. Players and their win-loss relationships are modeled as a skill gap graph, which is an undirected weighted graph. The player embeddings are learned from the graph using a random walk-based graph embedding method and can reflect the relative skill levels among players. Embeddings are low-dimensional vector representations that can be conveniently applied to subsequent tasks while still preserving the topological relationships in a graph. In the latter part of this paper, Graphical Elo (GElo) is introduced as an application of player embeddings when rating player skills. GElo is an extension of the classic Elo rating system. It constructs a skill gap graph based on player match histories and learns player embeddings from it. Afterward, the rating scores that were calculated by Elo are adjusted according to player activeness and cosine similarities among player embeddings. GElo can be executed offline and in parallel, and it is non-intrusive to existing rating systems. Experiments on public datasets show that GElo makes a more reliable evaluation of player skill levels than vanilla Elo. The experimental results suggest potential applications of player embeddings in competitive games and events.
翻译:本文提出了一个学习竞技游戏和事件中玩家嵌入的框架。将玩家及其胜负关系建模为技能差异图,该图为一种无向带权图。使用基于随机游走的图嵌入方法从图中学习玩家嵌入,并反映出玩家之间的相对技能水平。嵌入是低维度的向量表示,可在后续任务中方便地应用,同时仍保留图形拓扑关系。本文后半部分引入了图形Elo (GElo),作为玩家嵌入在评估玩家技能水平时的应用。GElo 是经典的Elo评分系统的扩展,基于玩家的比赛历史构建技能差异图,并从中学习玩家嵌入。然后,根据玩家活跃度和玩家嵌入之间的余弦相似度调整由Elo计算出的评分分数。GElo 可离线且并行执行,对现有评分系统不会产生干扰。公共数据集上的实验表明,GElo比纯Elo更可靠地评估玩家技能水平。实验结果展示了玩家嵌入在竞技游戏和事件中的潜在应用。