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 significant player interests and disinterests while then quantitatively measuring preference strength to mitigate over-smoothing in graph convolutions. Second, Preference-informed Representation Generation (PRG) module leverages LLMs to generate contextualized descriptions of games and players by reasoning personal preferences from comparing global and personal interests, thereby refining representations of players and games. Experiments on \textcolor{black}{two Steam datasets} demonstrate CPGRec+'s superior accuracy and diversity over state-of-the-art models. The code is accessible at https://github.com/HsipingLi/CPGRec-Plus.

翻译：游戏行业的快速扩张需要适应其动态特性的先进推荐系统。现有基于图神经网络的方法主要优先考虑准确性而非多样性，忽略了两者固有的权衡关系。为解决该问题，我们先前提出了CPGRec这一面向平衡优化的游戏推荐系统。然而，CPGRec未能考虑玩家-游戏交互中的关键差异——这些交互在反映玩家个人偏好时具有不同重要性，且可能加剧GNN模型固有的过平滑问题。此外，现有方法未能充分利用大语言模型的推理能力与广泛知识来应对这些局限。为弥补这一差距，我们提出了两个新模块：其一，偏好感知边权重调整模块通过赋予带符号的边权重，定性区分显著的玩家兴趣与不感兴趣偏好，同时定量测量偏好强度以缓解图卷积中的过平滑现象；其二，偏好感知表征生成模块利用LLM通过比较全局与个人兴趣推理个人偏好，生成游戏与玩家的情境化描述，从而优化玩家与游戏表征。在两个Steam数据集上的实验表明，CPGRec+在准确性和多样性方面均优于当前最优模型。代码已开源至https://github.com/HsipingLi/CPGRec-Plus。