Hierarchical Transformers for Group-Aware Sequential Recommendation: Application in MOBA Games

In recent years, several recommendation systems have been introduced to improve the user experience of players in video games. In Multiplayer Online Battle Arena (MOBA) games, a popular game genre, these systems are useful for recommending items for a character during a match. Current approaches focus on recommending a fixed set of items based on a character and the other participants. However, a MOBA match is an inherently sequential process, where its past decisions define the current ones. Therefore, it would be desirable to obtain a contextual recommendation considering a specific situation and the previously consumed items. To fill this gap, in this work, we propose HT4Rec for group-aware sequential item recommendation. It consists of a contextual encoder that generates a character-item representation contextualized by the other participants involved in the game, followed by a sequential encoder that captures sequential patterns of the data to recommend the next item. In this way, HT4Rec provides a flexible and unified attention-based network structure to capture both general and long-term preferences. Our evaluations on a Dota 2 video game dataset demonstrate that HT4Rec outperforms well-known sequential recommendation methods on various evaluation metrics. Additional experiments unveil the most important parts of our model and the most relevant inputs according to the attention mechanism, which could be used to interpret the suggested items. Furthermore, we demonstrate that HT4Rec could be applied to a different scenario (movie recommendation) than MOBA games, showing better results than the baseline models.