# User-controllable Recommendation Against Filter Bubbles

arXiv: https://arxiv.org/abs/2204.13844 · HTML: https://mlsanity.com/paper/2204.13844/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2022.

## Built on by (most cited first, 22 of 22)

- [Large Language Models for Generative Recommendation: A Survey and Visionary Discussions](https://mlsanity.com/paper/2309.01157/) (2023)
- [A Survey of Controllable Learning: Methods and Applications in Information Retrieval](https://mlsanity.com/paper/2407.06083/) (2024)
- [RecGPT Technical Report](https://mlsanity.com/paper/2507.22879/) (2025)
- [General Debiasing for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2307.10511/) (2023)
- [Interactive Recommendation Agent with Active User Commands](https://mlsanity.com/paper/2509.21317/) (2025)
- [CausalMed: Causality-Based Personalized Medication Recommendation Centered on Patient health state](https://mlsanity.com/paper/2404.12228/) (2024)
- [Uncertain Multi-Objective Recommendation via Orthogonal Meta-Learning Enhanced Bayesian Optimization](https://mlsanity.com/paper/2502.13180/) (2025)
- [Break Out of a Pigeonhole: A Unified Framework for Examining Miscalibration, Bias, and Stereotype in Recommender Systems](https://mlsanity.com/paper/2312.17443/) (2023)
- [Topic-Level Bayesian Surprise and Serendipity for Recommender Systems](https://mlsanity.com/paper/2308.06368/) (2023)
- [Recommendation with User Active Disclosing Willingness](https://mlsanity.com/paper/2211.01155/) (2022)
- [BHEISR: Nudging from Bias to Balance -- Promoting Belief Harmony by Eliminating Ideological Segregation in Knowledge-based Recommendations](https://mlsanity.com/paper/2307.02797/) (2023)
- [Separating and Learning Latent Confounders to Enhancing User Preferences Modeling](https://mlsanity.com/paper/2311.03381/) (2023)
- [Explainable History Distillation by Marked Temporal Point Process](https://mlsanity.com/paper/2311.07797/) (2023)
- [Causality Enhancement for Cross-Domain Recommendation](https://mlsanity.com/paper/2510.14641/) (2025)
- [Diversity Recommendation via Causal Deconfounding of Co-purchase Relations and Counterfactual Exposure](https://mlsanity.com/paper/2512.17733/) (2025)
- [SteerEval: A Framework for Evaluating Steerability with Natural Language Profiles for Recommendation](https://mlsanity.com/paper/2601.21105/) (2026)
- [RecBundle: A Next-Generation Geometric Paradigm for Explainable Recommender Systems](https://mlsanity.com/paper/2603.16088/) (2026)
- [Transparent and Controllable Recommendation Filtering via Multimodal Multi-Agent Collaboration](https://mlsanity.com/paper/2604.17459/) (2026)
- [From Passive Feeds to Guided Discovery: AI-Initiated Interaction for Vague Intent in Content Exploration](https://mlsanity.com/paper/2605.02902/) (2026)
- [Can We Steer the Black-Box? Towards Controllability-Centric Evaluation of Recommender Systems with Collaborative Agents](https://mlsanity.com/paper/2607.13418/) (2026)
- [How Does Empowering Users with Greater System Control Affect News Filter Bubbles?](https://mlsanity.com/paper/2607.15284/) (2026)
- [FacetCRS: Multi-Faceted Preference Learning for Pricking Filter Bubbles in Conversational Recommender System](https://mlsanity.com/paper/2609.20175/) (2026)

## Abstract (arXiv)

Recommender systems usually face the issue of filter bubbles: overrecommending homogeneous items based on user features and historical interactions. Filter bubbles will grow along the feedback loop and inadvertently narrow user interests. Existing work usually mitigates filter bubbles by incorporating objectives apart from accuracy such as diversity and fairness. However, they typically sacrifice accuracy, hurting model fidelity and user experience. Worse still, users have to passively accept the recommendation strategy and influence the system in an inefficient manner with high latency, e.g., keeping providing feedback (e.g., like and dislike) until the system recognizes the user intention. This work proposes a new recommender prototype called UserControllable Recommender System (UCRS), which enables users to actively control the mitigation of filter bubbles. Functionally, 1) UCRS can alert users if they are deeply stuck in filter bubbles. 2) UCRS supports four kinds of control commands for users to mitigate the bubbles at different granularities. 3) UCRS can respond to the controls and adjust the recommendations on the fly. The key to adjusting lies in blocking the effect of out-of-date user representations on recommendations, which contains historical information inconsistent with the control commands. As such, we develop a causality-enhanced User-Controllable Inference (UCI) framework, which can quickly revise the recommendations based on user controls in the inference stage and utilize counterfactual inference to mitigate the effect of out-of-date user representations. Experiments on three datasets validate that the UCI framework can effectively recommend more desired items based on user controls, showing promising performance w.r.t. both accuracy and diversity.