Interpretable Contextual Team-aware Item Recommendation: Application in Multiplayer Online Battle Arena Games

Abstract

The video game industry has adopted recommendation systems to boost users interest with a focus on game sales. Other exciting applications within video games are those that help the player make decisions that would maximize their playing experience, which is a desirable feature in real-time strategy video games such as Multiplayer Online Battle Arena (MOBA) like as DotA and LoL. Among these tasks, the recommendation of items is challenging, given both the contextual nature of the game and how it exposes the dependence on the formation of each team. Existing works on this topic do not take advantage of all the available contextual match data and dismiss potentially valuable information. To address this problem we develop TTIR, a contextual recommender model derived from the Transformer neural architecture that suggests a set of items to every team member, based on the contexts of teams and roles that describe the match. TTIR outperforms several approaches and provides interpretable recommendations through visualization of attention weights. Our evaluation indicates that both the Transformer architecture and the contextual information are essential to get the best results for this item recommendation task. Furthermore, a preliminary user survey indicates the usefulness of attention weights for explaining recommendations as well as ideas for future work. The code and dataset are available at: https://github.com/ojedaf/IC-TIR-Lol.

Multi‐view rank‐based random forest: A new algorithm for prediction in eSports

“…These data allow researchers to use ML methods to predict game outcome (Maymin, 2021), to cluster players (Mora-Cantallops & Sicilia, 2018), to improve the effectiveness of coaching services (Kim et al, 2021) and to recommend items (Araujo et al, 2019;Villa et al, 2020). To the best of our knowledge, this study is the first study that applies MVL technique to predict the match outcome of the game LoL in eSports.…”

“…• Experiment 4-We compared our results with the results presented in the state-of-art studies (Araujo et al, 2019;Cardoso, 2019;Chandrasekaran et al, 2020;Villa et al, 2020) on the same dataset.…”

“…The experimental results show that the proposed MVRRF method outperforms the RF method in terms of classification accuracy. We also compared our results with the results presented in the state-of-art studies (Araujo et al, 2019;Cardoso, 2019;Chandrasekaran et al, 2020;Villa et al, 2020) on the same dataset. According to the results, the proposed method achieved higher performance than the rest.…”

Relevant Independent Variables on MOBA Video Games to Train Machine Learning Algorithms

“…• To detect an experienced player based in the way that he navigates across the game map [18] [39] [41] [43]. • To develop rational agents which determine the best way to navigate the game map at a specific moment of the game [9] [10] [35].…”

“…The researches that study this game characteristic were grouped within the category objects. These seek to develop systems that recommend to the player which are the objects to acquire during the game [13] [69] [19] [35].…”