TL;DR
CPGRec+ is a novel framework that balances accuracy and diversity in personalized video game recommendations by incorporating preference-aware graph reweighting and LLM-based representation generation.
Contribution
It introduces two modules, PER and PRG, to improve the trade-off between accuracy and diversity in GNN-based game recommendation systems, leveraging large language models.
Findings
CPGRec+ outperforms state-of-the-art models in accuracy and diversity on Steam datasets.
Preference-informed edge reweighting mitigates over-smoothing in graph convolutions.
LLM-based description generation enhances personalization of recommendations.
Abstract
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 (LLMs) in addressing these limitations. To bridge this gap, we propose two new modules. First, Preference-informed Edge Reweighting (PER) module assigns signed edge weights to qualitatively distinguish…
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