{"title":"基于关系感知图注意网络的MOBA游戏道具推荐","authors":"Lijuan Duan, Shuxin Li, Wenbo Zhang, Wenjian Wang","doi":"10.1109/CoG51982.2022.9893595","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":394281,"journal":{"name":"2022 IEEE Conference on Games (CoG)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"MOBA Game Item Recommendation via Relation-aware Graph Attention Network\",\"authors\":\"Lijuan Duan, Shuxin Li, Wenbo Zhang, Wenjian Wang\",\"doi\":\"10.1109/CoG51982.2022.9893595\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":394281,\"journal\":{\"name\":\"2022 IEEE Conference on Games (CoG)\",\"volume\":\"20 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-08-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE Conference on Games (CoG)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CoG51982.2022.9893595\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE Conference on Games (CoG)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CoG51982.2022.9893595","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.