基于关系感知图注意网络的MOBA游戏道具推荐

Lijuan Duan, Shuxin Li, Wenbo Zhang, Wenjian Wang
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引用次数: 3

摘要

基于图关注网络的推荐系统由于其出色的学习各种侧面信息的能力而受到越来越多的关注。然而,之前的工作通常侧重于游戏角色推荐,而不太关注道具。此外,随着比赛队伍的变化,角色使用的物品也可能发生变化。为了克服这些限制,我们提出了一种关系感知的图注意项目推荐方法。它考虑了角色和道具之间的关系。此外,图注意机制聚合了项目的嵌入,分析了项目对相关字符的影响,并在字符和项目之间分配了注意权重。在kaggle公共游戏数据集上进行的大量实验表明,与其他现有方法相比,我们的方法在精度、F1和MAP方面明显优于先前的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MOBA Game Item Recommendation via Relation-aware Graph Attention Network
Recommender systems based on graph attention networks have received increasing attention due to their excellent ability to learn various side information. However, previous work usually focused on game character recommendation without paying much attention to items. In addition, as the team of the match changes, the items used by the characters may also change. To overcome these limitations, we propose a relation-aware graph attention item recommendation method. It considers the relationship between characters and items. Furthermore, the graph attention mechanism aggregates the embeddings of items and analyzes the effects of items on related characters while assigning attention weights between characters and items. Extensive experiments on the kaggle public game dataset show that our method significantly outperforms previous methods in terms of Precision, F1 and MAP compared to other existing methods.
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