Improved Dota2 Lineup Recommendation Model Based on a Bidirectional LSTM

作     者：Lei Zhang Chenbo Xu Yihua Gao Yi Han Xiaojiang Du Zhihong Tian Lei Zhang;Chenbo Xu;Yihua Gao;Yi Han;Xiaojiang Du;Zhihong Tian

作者机构：Henan Key Laboratory of Big Data Analysis and ProcessingKaifeng 457004China Institute of Data and Knowledge EngineeringHenan UniversityKaifeng 475004.China National Internet Emergency CenterZhengzhou 450000China Department of Computer and Information SciencesTemple UniversityPhiladelphiaPA 19122-6008USA Cyberspace Institute of Advanced TechnologyGuangzhou UniversityGuangzhou 510006China 

出 版 物：《Tsinghua Science and Technology》 (清华大学学报（自然科学版（英文版）)

年 卷 期：2020年第25卷第6期

页      面：712-720页

核心收录：

学科分类：04[教育学] 

基　　金：the Guangdong Province Key Research and Development Plan(No.2019B010137004) the National Natural Science Foundation of China(Nos.61402149 and 61871140) the Scientific and Technological Project of Henan Province(Nos.182102110065,182102210238,and 202102310340) the Natural Science Foundation of Henan Educational Committee(No.17B520006) Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme(2019) Foundation of University Young Key Teacher of Henan Province(No.2019GGJS040)。 

主　　题：Word2vec mutiplayer online battle arena games Continuous Bag Of Words(CBOW)model Long Short-Term Memory(LSTM) 

摘      要：In recent years,e-sports has rapidly developed,and the industry has produced large amounts of data with specifications,and these data are easily to be obtained.Due to the above characteristics,data mining and deep learning methods can be used to guide players and develop appropriate strategies to win games.As one of the world’s most famous e-sports events,Dota2 has a large audience base and a good game system.A victory in a game is often associated with a hero’s match,and players are often unable to pick the best lineup to compete.To solve this problem,in this paper,we present an improved bidirectional Long Short-Term Memory(LSTM)neural network model for Dota2 lineup recommendations.The model uses the Continuous Bag Of Words(CBOW)model in the Word2 vec model to generate hero vectors.The CBOW model can predict the context of a word in a sentence.Accordingly,a word is transformed into a hero,a sentence into a lineup,and a word vector into a hero vector,the model applied in this article recommends the last hero according to the first four heroes selected first,thereby solving a series of recommendation problems.