  The growing popularity of subscription services in video game consumption has
emphasized the importance of offering diversified recommendations. Providing
users with a diverse range of games is essential for ensuring continued
engagement and fostering long-term subscriptions. However, existing
recommendation models face challenges in effectively handling highly imbalanced
implicit feedback in gaming interactions. Additionally, they struggle to take
into account the distinctive characteristics of multiple categories and the
latent user interests associated with these categories. In response to these
challenges, we propose a novel framework, named DRGame, to obtain diversified
recommendation. It is centered on multi-category video games, consisting of two
{components}: Balance-driven Implicit Preferences Learning for data
pre-processing and Clustering-based Diversified Recommendation {Module} for
final prediction. The first module aims to achieve a balanced representation of
implicit feedback in game time, thereby discovering a comprehensive view of
player interests across different categories. The second module adopts
category-aware representation learning to cluster and select players and games
based on balanced implicit preferences, and then employs asymmetric neighbor
aggregation to achieve diversified recommendations. Experimental results on a
real-world dataset demonstrate the superiority of our proposed method over
existing approaches in terms of game diversity recommendations.
