# Trust and Trustworthiness in Social Recommender Systems

arXiv: https://arxiv.org/abs/1903.01780 · HTML: https://mlsanity.com/paper/1903.01780/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2019.

## Built on by (most cited first, 5 of 5)

- [Diversified Recommendations for Agents with Adaptive Preferences](https://mlsanity.com/paper/2210.07773/) (2022)
- [Learning with Exposure Constraints in Recommendation Systems](https://mlsanity.com/paper/2302.01377/) (2023)
- [KHAN: Knowledge-Aware Hierarchical Attention Networks for Accurate Political Stance Prediction](https://mlsanity.com/paper/2302.12126/) (2023)
- [Survey for Landing Generative AI in Social and E-commerce Recsys -- the Industry Perspectives](https://mlsanity.com/paper/2406.06475/) (2024)
- [Socially-Aware Recommender Systems Mitigate Opinion Clusterization](https://mlsanity.com/paper/2601.02412/) (2026)

## Abstract (arXiv)

The prevalence of misinformation on online social media has tangible empirical connections to increasing political polarization and partisan antipathy in the United States. Ranking algorithms for social recommendation often encode broad assumptions about network structure (like homophily) and group cognition (like, social action is largely imitative). Assumptions like these can be na"ive and exclusionary in the era of fake news and ideological uniformity towards the political poles. We examine these assumptions with aid from the user-centric framework of trustworthiness in social recommendation. The constituent dimensions of trustworthiness (diversity, transparency, explainability, disruption) highlight new opportunities for discouraging dogmatization and building decision-aware, transparent news recommender systems.