Improved Dota2 lineup recommendation model based on a bidirectional LSTM

Abstract

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.