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要求推荐系统能够适配其动态场景。现有基于图神经网络（GNN）的方法主要优先考虑准确性而非多样性，忽视了两者间的固有权衡。为解决这一问题，我们先前提出了面向平衡性的游戏推荐系统CPGRec。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程度的显著性，且可能加剧GNN模型中固有的过平滑问题。此外，现有方法未能充分利用大语言模型（LLMs）的推理能力和广泛知识来应对这些局限。为弥补这一差距，我们提出两个新模块。首先，偏好感知边重加权（PER）模块为边的符号权重赋值，以定性区分显著的玩家兴趣与无兴趣，进而定量衡量偏好强度以缓解图卷积中的过平滑问题。其次，偏好感知表征生成（PRG）模块利用LLMs通过比较全局与个人兴趣来推理个人偏好，从而生成游戏和玩家上下文化描述，进而精炼玩家与游戏的表征。在两个Steam数据集上的实验表明，CPGRec+相较于现有最优模型在准确性与多样性方面均表现更优。代码已公开于https://github.com/HsipingLi/CPGRec-Plus。