Large-scale Personalized Video Game Recommendation via Social-aware Contextualized Graph Neural Network
Large-scale Personalized Video Game Recommendation via Social-aware Contextualized Graph Neural Network
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DOI：
10.1145/3485447.3512273
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发表时间：
2022-02
期刊：
影响因子：
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通讯作者：
Liangwei Yang;Zhiwei Liu;Yu Wang;Chen Wang;Ziwei Fan;Philip S. Yu
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文献类型：
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作者：
Liangwei Yang;Zhiwei Liu;Yu Wang;Chen Wang;Ziwei Fan;Philip S. Yu
随着网络游戏的大量涌现，网络游戏推荐系统对用户和网络游戏平台来说都是必不可少的。前者可以发现更多自己感兴趣的潜在在线游戏，后者可以吸引用户在平台上停留更长时间。本文研究了Steam平台上在线游戏的用户行为特征。基于观察，我们认为，一个令人满意的在线游戏推荐系统能够表征：个性化，游戏情境化和社会联系。然而，同时解决所有问题对于游戏推荐来说是相当具有挑战性的。首先，个性化的游戏推荐需要加入的逗留时间的参与游戏，这是在现有的方法中被忽略。其次，游戏情境化应该反映这些关系的复杂性和高阶性。最后但并非最不重要的是，由于社交关系中的大量噪音，直接使用社交关系进行游戏推荐是有问题的。为此，我们提出了一个社会感知的上下文图神经推荐系统（SCGRec），它利用三个角度来提高游戏推荐。我们对用户的网络游戏行为进行了全面的分析，这就激发了在网络游戏推荐中处理这三个特征的必要性。
Because of the large number of online games available nowadays, online game recommender systems are necessary for users and online game platforms. The former can discover more potential online games of their interests, and the latter can attract users to dwell longer in the platform. This paper investigates the characteristics of user behaviors with respect to the online games on the Steam platform. Based on the observations, we argue that a satisfying recommender system for online games is able to characterize: personalization, game contextualization and social connection. However, simultaneously solving all is rather challenging for game recommendation. Firstly, personalization for game recommendation requires the incorporation of the dwelling time of engaged games, which are ignored in existing methods. Secondly, game contextualization should reflect the complex and high-order properties of those relations. Last but not least, it is problematic to use social connections directly for game recommendations due to the massive noise within social connections. To this end, we propose a Social-aware Contextualized Graph Neural Recommender System (SCGRec), which harnesses three perspectives to improve game recommendation. We conduct a comprehensive analysis of users’ online game behaviors, which motivates the necessity of handling those three characteristics in the online game recommendation.