# Trust and Transparency in Recommender Systems

arXiv: https://arxiv.org/abs/2304.08094 · HTML: https://mlsanity.com/paper/2304.08094/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 52 references that are in the map.
- First posted on arXiv 17 Apr 2023.

## Builds on (most cited first, 8 of 8)

- [A Survey on Conversational Recommender Systems](https://mlsanity.com/paper/2004.00646/) (2020)
- [Making deep neural networks right for the right scientific reasons by interacting with their explanations](https://mlsanity.com/paper/2001.05371/) (2020)
- [One Explanation Does Not Fit All: The Promise of Interactive Explanations for Machine Learning Transparency](https://mlsanity.com/paper/2001.09734/) (2020)
- [A systematic review and taxonomy of explanations in decision support and recommender systems](https://mlsanity.com/paper/2006.08672/) (2020)
- [On the Effect of Information Asymmetry in Human-AI Teams](https://mlsanity.com/paper/2205.01467/) (2022)
- [Are we measuring trust correctly in explainability, interpretability, and transparency research?](https://mlsanity.com/paper/2209.00651/) (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)

## Built on by (most cited first, 3 of 3)

- [Interactive Explanation with Varying Level of Details in an Explainable Scientific Literature Recommender System](https://mlsanity.com/paper/2306.05809/) (2023)
- [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)

Trust is long recognized to be an important factor in Recommender Systems (RS). However, there are different perspectives on trust and different ways to evaluate it. Moreover, a link between trust and transparency is often assumed but not always further investigated. In this paper we first go through different understandings and measurements of trust in the AI and RS community, such as demonstrated and perceived trust. We then review the relationsships between trust and transparency, as well as mental models, and investigate different strategies to achieve transparency in RS such as explanation, exploration and exploranation (i.e., a combination of exploration and explanation). We identify a need for further studies to explore these concepts as well as the relationships between them.