In recent years, the video game industry has experienced substantial growth, presenting players with a vast array of game choices. This surge in options has spurred the need for a specialized recommender system tailored for video games. However, current video game recommendation approaches tend to prioritize accuracy over diversity, potentially leading to unvaried game suggestions. In addition, the existing game recommendation methods commonly lack the ability to establish strict connections between games to enhance accuracy. Furthermore, many existing diversity-focused methods fail to leverage crucial item information, such as item category and popularity during neighbor modeling and message propagation. To address these challenges, we introduce a novel framework, called CPGRec, comprising three modules, namely accuracy-driven, diversity-driven, and comprehensive modules. The first module extends the state-of-the-art accuracy-focused game recommendation method by connecting games in a more stringent manner to enhance recommendation accuracy. The second module connects neighbors with diverse categories within the proposed game graph and harnesses the advantages of popular game nodes to amplify the influence of long-tail games within the player-game bipartite graph, thereby enriching recommendation diversity. The third module combines the above two modules and employs a new negative-sample rating score reweighting method to balance accuracy and diversity. Experimental results on the Steam dataset demonstrate the effectiveness of our proposed method in improving game recommendations. The dataset and source codes are anonymously released at: https://github.com/CPGRec2024/CPGRec.git.

翻译：近年来，视频游戏产业经历了显著增长，为玩家提供了海量游戏选择。这种选项激增催生了针对视频游戏的专用推荐系统需求。然而，当前视频游戏推荐方法往往优先考虑准确度而非多样性，可能导致游戏推荐缺乏变化。此外，现有游戏推荐方法普遍缺乏建立游戏间严格关联以提升准确性的能力。同时，许多现有以多样性为导向的方法在邻居建模与消息传播过程中未能利用关键物品信息（如物品类别与流行度）。为应对这些挑战，我们提出一个名为CPGRec的新型框架，包含三个模块：准确度驱动模块、多样性驱动模块与综合模块。第一模块通过更严格的方式连接游戏，扩展了当前最先进的以准确度为中心的游戏推荐方法，从而提升推荐准确性。第二模块在所提出的游戏图中连接具有多样类别的邻居，并利用热门游戏节点的优势增强玩家-游戏二分图中长尾游戏的影响力，进而丰富推荐多样性。第三模块融合上述两个模块，并采用新的负样本评分重加权方法以实现准确度与多样性的平衡。在Steam数据集上的实验结果表明，我们提出的方法在改善游戏推荐方面具有有效性。数据集与源代码已匿名发布于：https://github.com/CPGRec2024/CPGRec.git。