# Interactive Explanation with Varying Level of Details in an Explainable Scientific Literature Recommender System

arXiv: https://arxiv.org/abs/2306.05809 · HTML: https://mlsanity.com/paper/2306.05809/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 86 references that are in the map.
- First posted on arXiv 9 Jun 2023.

## Builds on (most cited first, 17 of 17)

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Questioning the AI: Informing Design Practices for Explainable AI User Experiences](https://mlsanity.com/paper/2001.02478/) (2020)
- [Explainable Recommendation: A Survey and New Perspectives](https://mlsanity.com/paper/1804.11192/) (2018)
- [A Survey on Knowledge Graph-Based Recommender Systems](https://mlsanity.com/paper/2003.00911/) (2020)
- [A Survey on Conversational Recommender Systems](https://mlsanity.com/paper/2004.00646/) (2020)
- [Human-Centered Explainable AI (XAI): From Algorithms to User Experiences](https://mlsanity.com/paper/2110.10790/) (2021)
- [AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap](https://mlsanity.com/paper/2306.01941/) (2023)
- [Human-centered Explainable AI: Towards a Reflective Sociotechnical Approach](https://mlsanity.com/paper/2002.01092/) (2020)
- [explAIner: A Visual Analytics Framework for Interactive and Explainable Machine Learning](https://mlsanity.com/paper/1908.00087/) (2019)
- [One Explanation Does Not Fit All: The Promise of Interactive Explanations for Machine Learning Transparency](https://mlsanity.com/paper/2001.09734/) (2020)
- [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://mlsanity.com/paper/2006.11316/) (2020)
- [Trust in AI and Its Role in the Acceptance of AI Technologies](https://mlsanity.com/paper/2203.12687/) (2022)
- [Is More Always Better? The Effects of Personal Characteristics and Level of Detail on the Perception of Explanations in a Recommender System](https://mlsanity.com/paper/2304.00969/) (2023)
- [Effects of interactivity and presentation on review-based explanations for recommendations](https://mlsanity.com/paper/2105.11794/) (2021)
- [Justification vs. Transparency: Why and How Visual Explanations in a Scientific Literature Recommender System](https://mlsanity.com/paper/2305.17034/) (2023)
- [Trust and Transparency in Recommender Systems](https://mlsanity.com/paper/2304.08094/) (2023)

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

- [Improving Human Verification of LLM Reasoning through Interactive Explanation Interfaces](https://mlsanity.com/paper/2510.22922/) (2025)
- [May I Ask a Follow-up Question? Understanding the Benefits of Conversations in Neural Network Explainability](https://mlsanity.com/paper/2309.13965/) (2023)
- [Explanation User Interfaces: A Systematic Literature Review](https://mlsanity.com/paper/2505.20085/) (2025)
- [Visual or Textual: Effects of Explanation Format and Personal Characteristics on the Perception of Explanations in an Educational Recommender System](https://mlsanity.com/paper/2603.25624/) (2026)
- [Investigating the Effects of Different Levels of User Control in an Interactive Educational Recommender System](https://mlsanity.com/paper/2605.01400/) (2026)

## Abstract (arXiv)

Explainable recommender systems (RS) have traditionally followed a one-size-fits-all approach, delivering the same explanation level of detail to each user, without considering their individual needs and goals. Further, explanations in RS have so far been presented mostly in a static and non-interactive manner. To fill these research gaps, we aim in this paper to adopt a user-centered, interactive explanation model that provides explanations with different levels of detail and empowers users to interact with, control, and personalize the explanations based on their needs and preferences. We followed a user-centered approach to design interactive explanations with three levels of detail (basic, intermediate, and advanced) and implemented them in the transparent Recommendation and Interest Modeling Application (RIMA). We conducted a qualitative user study (N=14) to investigate the impact of providing interactive explanations with varying level of details on the users' perception of the explainable RS. Our study showed qualitative evidence that fostering interaction and giving users control in deciding which explanation they would like to see can meet the demands of users with different needs, preferences, and goals, and consequently can have positive effects on different crucial aspects in explainable recommendation, including transparency, trust, satisfaction, and user experience.