# Is More Always Better? The Effects of Personal Characteristics and Level of Detail on the Perception of Explanations in a Recommender System

arXiv: https://arxiv.org/abs/2304.00969 · HTML: https://mlsanity.com/paper/2304.00969/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 40 references that are in the map.
- First posted on arXiv 3 Apr 2023.

## Builds on (most cited first, 3 of 3)

- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Explainable Recommendation: A Survey and New Perspectives](https://mlsanity.com/paper/1804.11192/) (2018)
- [A systematic review and taxonomy of explanations in decision support and recommender systems](https://mlsanity.com/paper/2006.08672/) (2020)

## Built on by (most cited first, 8 of 8)

- [Towards Explainable Temporal User Profiling with LLMs](https://mlsanity.com/paper/2505.00886/) (2025)
- [Interactive Explanation with Varying Level of Details in an Explainable Scientific Literature Recommender System](https://mlsanity.com/paper/2306.05809/) (2023)
- [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)
- [Navigating User Experience of ChatGPT-based Conversational Recommender Systems: The Effects of Prompt Guidance and Recommendation Domain](https://mlsanity.com/paper/2405.13560/) (2024)
- [Whom do Explanations Serve? A Systematic Literature Survey of User Characteristics in Explainable Recommender Systems Evaluation](https://mlsanity.com/paper/2412.14193/) (2024)
- [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)

Despite the acknowledgment that the perception of explanations may vary considerably between end-users, explainable recommender systems (RS) have traditionally followed a one-size-fits-all model, whereby the same explanation level of detail is provided to each user, without taking into consideration individual user's context, i.e., goals and personal characteristics. To fill this research gap, we aim in this paper at a shift from a one-size-fits-all to a personalized approach to explainable recommendation by giving users agency in deciding which explanation they would like to see. We developed a transparent Recommendation and Interest Modeling Application (RIMA) that provides on-demand personalized explanations of the recommendations, with three levels of detail (basic, intermediate, advanced) to meet the demands of different types of end-users. We conducted a within-subject study (N=31) to investigate the relationship between user's personal characteristics and the explanation level of detail, and the effects of these two variables on the perception of the explainable RS with regard to different explanation goals. Our results show that the perception of explainable RS with different levels of detail is affected to different degrees by the explanation goal and user type. Consequently, we suggested some theoretical and design guidelines to support the systematic design of explanatory interfaces in RS tailored to the user's context.