# Modelling High-Order Social Relations for Item Recommendation

arXiv: https://arxiv.org/abs/2003.10149 · HTML: https://mlsanity.com/paper/2003.10149/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2020.

## Built on by (most cited first, 13 of 13)

- [A Survey of Graph Neural Networks for Social Recommender Systems](https://mlsanity.com/paper/2212.04481/) (2022)
- [A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation](https://mlsanity.com/paper/2104.13030/) (2021)
- [Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networks](https://mlsanity.com/paper/2208.10364/) (2022)
- [Score-based Generative Diffusion Models for Social Recommendations](https://mlsanity.com/paper/2412.15579/) (2024)
- [CirT: Global Subseasonal-to-Seasonal Forecasting with Geometry-inspired Transformer](https://mlsanity.com/paper/2502.19750/) (2025)
- [Spectral Adversarial Training for Robust Graph Neural Network](https://mlsanity.com/paper/2211.10896/) (2022)
- [Heterogeneous Graph Neural Network for Privacy-Preserving Recommendation](https://mlsanity.com/paper/2210.00538/) (2022)
- [HSR: Hyperbolic Social Recommender](https://mlsanity.com/paper/2102.09389/) (2021)
- [A Survey of Cross-domain Graph Learning: Progress and Future Directions](https://mlsanity.com/paper/2503.11086/) (2025)
- [Relaxing Continuous Constraints of Equivariant Graph Neural Networks for Physical Dynamics Learning](https://mlsanity.com/paper/2406.16295/) (2024)
- [Mini-Game Lifetime Value Prediction in WeChat](https://mlsanity.com/paper/2506.11037/) (2025)
- [Oversmoothing: A Nightmare for Graph Contrastive Learning?](https://mlsanity.com/paper/2306.02117/) (2023)
- [Towards Practical Large-scale Dynamical Heterogeneous Graph Embedding: Cold-start Resilient Recommendation](https://mlsanity.com/paper/2512.13120/) (2025)

## Abstract (arXiv)

The prevalence of online social network makes it compulsory to study how social relations affect user choice. However, most existing methods leverage only first-order social relations, that is, the direct neighbors that are connected to the target user. The high-order social relations, e.g., the friends of friends, which very informative to reveal user preference, have been largely ignored. In this work, we focus on modeling the indirect influence from the high-order neighbors in social networks to improve the performance of item recommendation. Distinct from mainstream social recommenders that regularize the model learning with social relations, we instead propose to directly factor social relations in the predictive model, aiming at learning better user embeddings to improve recommendation. To address the challenge that high-order neighbors increase dramatically with the order size, we propose to recursively "propagate" embeddings along the social network, effectively injecting the influence of high-order neighbors into user representation. We conduct experiments on two real datasets of Yelp and Douban to verify our High-Order Social Recommender (HOSR) model. Empirical results show that our HOSR significantly outperforms recent graph regularization-based recommenders NSCR and IF-BPR+, and graph convolutional network-based social influence prediction model DeepInf, achieving new state-of-the-arts of the task.