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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.
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.
This study was partly supported by 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), and Foundation of University Young Key Teacher of Henan Province (No. 2019GGJS040).
The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).