# The closed loop between opinion formation and personalised recommendations

arXiv: https://arxiv.org/abs/1809.04644 · HTML: https://mlsanity.com/paper/1809.04644/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2018.

## Built on by (most cited first, 12 of 12)

- [Y Social: an LLM-powered Social Media Digital Twin](https://mlsanity.com/paper/2408.00818/) (2024)
- [Generative Slate Recommendation with Reinforcement Learning](https://mlsanity.com/paper/2301.08632/) (2023)
- [A Classification of Feedback Loops and Their Relation to Biases in Automated Decision-Making Systems](https://mlsanity.com/paper/2305.06055/) (2023)
- [Towards Psychologically-Grounded Dynamic Preference Models](https://mlsanity.com/paper/2208.01534/) (2022)
- [Harm Mitigation in Recommender Systems under User Preference Dynamics](https://mlsanity.com/paper/2406.09882/) (2024)
- [Incentivizing News Consumption on Social Media Platforms Using Large Language Models and Realistic Bot Accounts](https://mlsanity.com/paper/2403.13362/) (2024)
- [Re-ranking Using Large Language Models for Mitigating Exposure to Harmful Content on Social Media Platforms](https://mlsanity.com/paper/2501.13977/) (2025)
- [Echo-chambers and Idea Labs: Communication Styles on Twitter](https://mlsanity.com/paper/2403.19423/) (2024)
- [Content-Agnostic Moderation for Stance-Neutral Recommendation](https://mlsanity.com/paper/2405.18941/) (2024)
- [Knowledge Graph Representation for Political Information Sources](https://mlsanity.com/paper/2404.03437/) (2024)
- [The Nah Bandit: Modeling User Non-compliance in Recommendation Systems](https://mlsanity.com/paper/2408.07897/) (2024)
- [Socially-Aware Recommender Systems Mitigate Opinion Clusterization](https://mlsanity.com/paper/2601.02412/) (2026)

## Abstract (arXiv)

In online platforms, recommender systems are responsible for directing users to relevant contents. In order to enhance the users' engagement, recommender systems adapt their output to the reactions of the users, who are in turn affected by the recommended contents. In this work, we study a tractable analytical model of a user that interacts with an online news aggregator, with the purpose of making explicit the feedback loop between the evolution of the user's opinion and the personalised recommendation of contents. More specifically, we assume that the user is endowed with a scalar opinion about a certain issue and seeks news about it on a news aggregator: this opinion is influenced by all received news, which are characterized by a binary position on the issue at hand. The user is affected by a confirmation bias, that is, a preference for news that confirm her current opinion. The news aggregator recommends items with the goal of maximizing the number of user's clicks (as a measure of her engagement): in order to fulfil its goal, the recommender has to compromise between exploring the user's preferences and exploiting what it has learned so far. After defining suitable metrics for the effectiveness of the recommender systems (such as the click-through rate) and for its impact on the opinion, we perform both extensive numerical simulations and a mathematical analysis of the model. We find that personalised recommendations markedly affect the evolution of opinions and favor the emergence of more extreme ones: the intensity of these effects is inherently related to the effectiveness of the recommender. We also show that by tuning the amount of randomness in the recommendation algorithm, one can seek a balance between the effectiveness of the recommendation system and its impact on the opinions.