  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.
