# Filter Bubbles in Recommender Systems: Fact or Fallacy -- A Systematic Review

arXiv: https://arxiv.org/abs/2307.01221 · HTML: https://mlsanity.com/paper/2307.01221/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 113 references that are in the map.
- First posted on arXiv 2 Jul 2023.

## Builds on (most cited first, 13 of 13)

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Bias and Debias in Recommender System: A Survey and Future Directions](https://mlsanity.com/paper/2010.03240/) (2020)
- [Understanding Echo Chambers in E-commerce Recommender Systems](https://mlsanity.com/paper/2007.02474/) (2020)
- [PRINCE: Provider-side Interpretability with Counterfactual Explanations in Recommender Systems](https://mlsanity.com/paper/1911.08378/) (2019)
- [Recommenders with a mission: assessing diversity in newsrecommendations](https://mlsanity.com/paper/2012.10185/) (2020)
- [Equality of Learning Opportunity via Individual Fairness in Personalized Recommendations](https://mlsanity.com/paper/2006.04282/) (2020)
- [A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects](https://mlsanity.com/paper/2102.07654/) (2021)
- [Blockchain-based Recommender Systems: Applications, Challenges and Future Opportunities](https://mlsanity.com/paper/2111.11509/) (2021)
- [The emergence of Explainability of Intelligent Systems: Delivering Explainable and Personalised Recommendations for Energy Efficiency](https://mlsanity.com/paper/2010.04990/) (2020)
- [Is News Recommendation a Sequential Recommendation Task?](https://mlsanity.com/paper/2108.08984/) (2021)
- [Towards Analyzing the Bias of News Recommender Systems Using Sentiment and Stance Detection](https://mlsanity.com/paper/2203.05824/) (2022)
- [Optimizing Long-term Value for Auction-Based Recommender Systems via On-Policy Reinforcement Learning](https://mlsanity.com/paper/2305.13747/) (2023)
- [Diversification in Session-based News Recommender Systems](https://mlsanity.com/paper/2102.03265/) (2021)

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

- [AI in Thyroid Cancer Diagnosis: Techniques, Trends, and Future Directions](https://mlsanity.com/paper/2308.13592/) (2023)
- [Toward Holistic Evaluation of Recommender Systems Powered by Generative Models](https://mlsanity.com/paper/2504.06667/) (2025)
- [Algorithmic Drift: A Simulation Framework to Study the Effects of Recommender Systems on User Preferences](https://mlsanity.com/paper/2409.16478/) (2024)
- [Developing and Evaluating a Design Method for Positive Artificial Intelligence](https://mlsanity.com/paper/2402.01499/) (2024)
- [Machine Learning and Transformers for Thyroid Carcinoma Diagnosis: A Review](https://mlsanity.com/paper/2403.13843/) (2024)
- [LLM-Enhanced Reinforcement Learning for Long-Term User Satisfaction in Interactive Recommendation](https://mlsanity.com/paper/2601.19585/) (2026)
- [Beyond RMSE and MAE: Introducing EAUC to unmask hidden bias and unfairness in dyadic regression models](https://mlsanity.com/paper/2401.10690/) (2024)
- [Looking for Fairness in Recommender Systems](https://mlsanity.com/paper/2507.12242/) (2025)
- [Data Flows and Colonial Regimes in Africa: A Critical Analysis of the Colonial Futurities Embedded in AI Ecosystems](https://mlsanity.com/paper/2511.19283/) (2025)
- [Ideological Isolation in Online Social Networks: A Survey of Computational Definitions, Metrics, and Mitigation Strategies](https://mlsanity.com/paper/2601.07884/) (2026)
- [Magic, Madness, Heaven, Sin: LLM Output Diversity is Everything, Everywhere, All at Once](https://mlsanity.com/paper/2604.01504/) (2026)
- [Modeling User Exploration Saturation: When Recommender Systems Should Stop Pushing Novelty](https://mlsanity.com/paper/2604.16419/) (2026)
- [How Personal Characteristics Shape User Exploration of Diverse Movie Recommendations with a LLM-Based Multi-Agent System](https://mlsanity.com/paper/2604.24405/) (2026)

## Abstract (arXiv)

A filter bubble refers to the phenomenon where Internet customization effectively isolates individuals from diverse opinions or materials, resulting in their exposure to only a select set of content. This can lead to the reinforcement of existing attitudes, beliefs, or conditions. In this study, our primary focus is to investigate the impact of filter bubbles in recommender systems. This pioneering research aims to uncover the reasons behind this problem, explore potential solutions, and propose an integrated tool to help users avoid filter bubbles in recommender systems. To achieve this objective, we conduct a systematic literature review on the topic of filter bubbles in recommender systems. The reviewed articles are carefully analyzed and classified, providing valuable insights that inform the development of an integrated approach. Notably, our review reveals evidence of filter bubbles in recommendation systems, highlighting several biases that contribute to their existence. Moreover, we propose mechanisms to mitigate the impact of filter bubbles and demonstrate that incorporating diversity into recommendations can potentially help alleviate this issue. The findings of this timely review will serve as a benchmark for researchers working in interdisciplinary fields such as privacy, artificial intelligence ethics, and recommendation systems. Furthermore, it will open new avenues for future research in related domains, prompting further exploration and advancement in this critical area.