# Recommender Systems for Online and Mobile Social Networks: A survey

arXiv: https://arxiv.org/abs/2307.01207 · HTML: https://mlsanity.com/paper/2307.01207/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 181 references that are in the map.
- First posted on arXiv 28 Jun 2023.

## Builds on (most cited first, 7 of 7)

- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Empirical Analysis of Predictive Algorithms for Collaborative Filtering](https://mlsanity.com/paper/1301.7363/) (2013)
- [A Location-Sentiment-Aware Recommender System for Both Home-Town and Out-of-Town Users](https://mlsanity.com/paper/1706.06239/) (2017)
- [The Internet of People: A human and data-centric paradigm for the Next Generation Internet](https://mlsanity.com/paper/2205.13970/) (2022)
- [A Personalized Recommender System for Pervasive Social Networks](https://mlsanity.com/paper/2205.15063/) (2022)
- [Network-based recommendation algorithms: A review](https://mlsanity.com/paper/1511.06252/) (2015)
- [The Internet of People (IoP): A New Wave in Pervasive Mobile Computing](https://mlsanity.com/paper/2205.14156/) (2022)

## Built on by (most cited first, 3 of 3)

- [Rec-R1: Bridging Generative Large Language Models and User-Centric Recommendation Systems via Reinforcement Learning](https://mlsanity.com/paper/2503.24289/) (2025)
- [ContextGNN: Beyond Two-Tower Recommendation Systems](https://mlsanity.com/paper/2411.19513/) (2024)
- [A Survey of Graph Unlearning](https://mlsanity.com/paper/2310.02164/) (2023)

## Abstract (arXiv)

Recommender Systems (RS) currently represent a fundamental tool in online services, especially with the advent of Online Social Networks (OSN). In this case, users generate huge amounts of contents and they can be quickly overloaded by useless information. At the same time, social media represent an important source of information to characterize contents and users' interests. RS can exploit this information to further personalize suggestions and improve the recommendation process. In this paper we present a survey of Recommender Systems designed and implemented for Online and Mobile Social Networks, highlighting how the use of social context information improves the recommendation task, and how standard algorithms must be enhanced and optimized to run in a fully distributed environment, as opportunistic networks. We describe advantages and drawbacks of these systems in terms of algorithms, target domains, evaluation metrics and performance evaluations. Eventually, we present some open research challenges in this area.