# The Limits of Popularity-Based Recommendations, and the Role of Social Ties

arXiv: https://arxiv.org/abs/1607.04263 · HTML: https://mlsanity.com/paper/1607.04263/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2016.

## Built on by (most cited first, 2 of 2)

- [CausPref: Causal Preference Learning for Out-of-Distribution Recommendation](https://mlsanity.com/paper/2202.03984/) (2022)
- [Content-Agnostic Moderation for Stance-Neutral Recommendation](https://mlsanity.com/paper/2405.18941/) (2024)

## Abstract (arXiv)

In this paper we introduce a mathematical model that captures some of the salient features of recommender systems that are based on popularity and that try to exploit social ties among the users. We show that, under very general conditions, the market always converges to a steady state, for which we are able to give an explicit form. Thanks to this we can tell rather precisely how much a market is altered by a recommendation system, and determine the power of users to influence others. Our theoretical results are complemented by experiments with real world social networks showing that social graphs prevent large market distortions in spite of the presence of highly influential users.