Sequential Item Recommendation in the MOBA Game Dota 2

TL;DR

This paper investigates the use of sequential item recommendation models to assist Dota 2 players in making purchase decisions, introducing a new dataset and analyzing model effectiveness.

Contribution

It introduces Dota-350k, a large dataset for Dota 2, and evaluates the effectiveness of various sequential recommendation models in this gaming context.

Findings

Order-aware models perform best in recommendations.

RNN-based models outperform Transformer models on Dota-350k.

Sequential models can effectively support item purchase decisions in Dota 2.

Abstract

Multiplayer Online Battle Arena (MOBA) games such as Dota 2 attract hundreds of thousands of players every year. Despite the large player base, it is still important to attract new players to prevent the community of a game from becoming inactive. Entering MOBA games is, however, often demanding, requiring the player to learn numerous skills at once. An important factor of success is buying the correct items which forms a complex task depending on various in-game factors such as already purchased items, the team composition, or available resources. A recommendation system can support players by reducing the mental effort required to choose a suitable item, helping, e.g., newer players or players returning to the game after a longer break, to focus on other aspects of the game. Since Sequential Item Recommendation (SIR) has proven to be effective in various domains (e.g. e-commerce, movie recommendation or playlist continuation), we explore the applicability of well-known SIR models in the context of purchase recommendations in Dota 2. To facilitate this research, we collect, analyze and publish Dota-350k, a new large dataset based on recent Dota 2 matches. We find that SIR models can be employed effectively for item recommendation in Dota 2. Our results show that models that consider the order of purchases are the most effective. In contrast to other domains, we find RNN-based models to outperform the more recent Transformer-based architectures on Dota-350k.