CPGRec+: A Balance-oriented Framework for Personalized Video Game Recommendations
The rapid expansion of gaming industry requires advanced recommender systems tailored to its dynamic landscape. Existing Graph Neural Network (GNN)-based methods primarily prioritize accuracy over diversity, overlooking their inherent trade-off. To address this, we previously proposed CPGRec, a balance-oriented gaming recommender system. However, CPGRec fails to account for critical disparities in player-game interactions, which carry varying significance in reflecting players' personal preferences and may exacerbate over-smoothness issues inherent in GNN-based models. Moreover, existing approaches underutilize the reasoning capabilities and extensive knowledge of large language models (L...
| Authors | Xiping Li, Aier Yang, Jianghong Ma et al. |
|---|---|
| Primary category | cs.IR |
| Published | 16 April 2026 |
| Last updated at source | 27 August 2026 |
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