DRGame: Diversified Recommendation for Multi-category Video Games with Balanced Implicit Preferences

Kangzhe Liu, Jianghong Ma, Shanshan Feng, Haijun Zhang, Zhao Zhang

Abstract: 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. 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 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.