DRGame: Diversified Recommendation for Multi-category Video Games with Balanced Implicit Preferences
Kangzhe Liu, Jianghong Ma, Shanshan Feng, Haijun Zhang, Zhao Zhang

TL;DR
DRGame is a novel framework that enhances diversified video game recommendations by balancing implicit feedback and considering multi-category characteristics, leading to improved game diversity in recommendations.
Contribution
It introduces a two-component framework with balanced implicit preferences learning and clustering-based diversification tailored for multi-category video games.
Findings
Outperforms existing methods in game diversity metrics
Effectively balances implicit feedback across categories
Enhances user engagement through diversified recommendations
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. 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…
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Taxonomy
TopicsRecommender Systems and Techniques · Digital Games and Media · Gambling Behavior and Treatments
