TY - JOUR
T1 - Improved Dota2 Lineup Recommendation Model Based on a Bidirectional LSTM
AU - Zhang, Lei
AU - Xu, Chenbo
AU - Gao, Yihua
AU - Han, Yi
AU - Du, Xiaojiang
AU - Tian, Zhihong
N1 - Publisher Copyright:
© 2020 American Society of Civil Engineers (ASCE). All rights reserved.
PY - 2020/12
Y1 - 2020/12
N2 - 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 Word2vec 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.
AB - 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 Word2vec 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.
KW - Continuous Bag Of Words (CBOW) model
KW - Long Short-Term Memory (LSTM)
KW - Word2vec
KW - mutiplayer online battle arena games
UR - http://www.scopus.com/inward/record.url?scp=85085088295&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85085088295&partnerID=8YFLogxK
U2 - 10.26599/TST.2019.9010065
DO - 10.26599/TST.2019.9010065
M3 - Article
AN - SCOPUS:85085088295
SN - 1007-0214
VL - 25
SP - 712
EP - 720
JO - Tsinghua Science and Technology
JF - Tsinghua Science and Technology
IS - 6
ER -