Large-scale Personalized Video Game Recommendation via Social-aware Contextualized Graph Neural Network

Abstract

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.

References in corpus (10)

• A Comprehensive Survey on Graph Neural Networks
• Neural Graph Collaborative Filtering
• Graph Convolutional Neural Networks for Web-Scale Recommender Systems
• KGAT: Knowledge Graph Attention Network for Recommendation
• Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems
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• ConsisRec: Enhancing GNN for Social Recommendation via Consistent Neighbor Aggregation
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Cited by in corpus (6)

• A Survey of Graph Neural Networks for Social Recommender Systems
• DGRec: Graph Neural Network for Recommendation with Diversified Embedding Generation
• Graph-based Alignment and Uniformity for Recommendation
• Dimension Independent Mixup for Hard Negative Sample in Collaborative Filtering
• Group Identification via Transitional Hypergraph Convolution with Cross-view Self-supervised Learning
• Graph-based Village Level Poverty Identification