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

arXiv: https://arxiv.org/abs/2202.03392 · HTML: https://mlsanity.com/paper/2202.03392/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2022.

## Built on by (most cited first, 14 of 14)

- [A Survey of Graph Neural Networks for Social Recommender Systems](https://mlsanity.com/paper/2212.04481/) (2022)
- [ContrastVAE: Contrastive Variational AutoEncoder for Sequential Recommendation](https://mlsanity.com/paper/2209.00456/) (2022)
- [Graph Collaborative Signals Denoising and Augmentation for Recommendation](https://mlsanity.com/paper/2304.03344/) (2023)
- [Generalizing Graph Transformers Across Diverse Graphs and Tasks via Pre-training](https://mlsanity.com/paper/2407.03953/) (2024)
- [Ranking-based Group Identification via Factorized Attention on Social Tripartite Graph](https://mlsanity.com/paper/2211.01830/) (2022)
- [View while Moving: Efficient Video Recognition in Long-untrimmed Videos](https://mlsanity.com/paper/2308.04834/) (2023)
- [Graph-based Alignment and Uniformity for Recommendation](https://mlsanity.com/paper/2308.09292/) (2023)
- [Sequential Recommendation with Auxiliary Item Relationships via Multi-Relational Transformer](https://mlsanity.com/paper/2210.13572/) (2022)
- [Episodes Discovery Recommendation with Multi-Source Augmentations](https://mlsanity.com/paper/2301.01737/) (2023)
- [Dual-Phase Playtime-guided Recommendation: Interest Intensity Exploration and Multimodal Random Walks](https://mlsanity.com/paper/2508.14058/) (2025)
- [Dimension Independent Mixup for Hard Negative Sample in Collaborative Filtering](https://mlsanity.com/paper/2306.15905/) (2023)
- [IDVT: Interest-aware Denoising and View-guided Tuning for Social Recommendation](https://mlsanity.com/paper/2308.15926/) (2023)
- [Group-Aware Interest Disentangled Dual-Training for Personalized Recommendation](https://mlsanity.com/paper/2311.09577/) (2023)
- [RankGraph-2: Lifecycle Co-Design for Billion-Node Graph Learning in Recommendation](https://mlsanity.com/paper/2606.18379/) (2026)

## Abstract (arXiv)

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.