# Designing Explanations for Group Recommender Systems

arXiv: https://arxiv.org/abs/2102.12413 · HTML: https://mlsanity.com/paper/2102.12413/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 73 references that are in the map.
- First posted on arXiv 24 Feb 2021.

## Builds on (most cited first, 2 of 2)

- [A systematic review and taxonomy of explanations in decision support and recommender systems](https://mlsanity.com/paper/2006.08672/) (2020)
- [An Efficient Diagnosis Algorithm for Inconsistent Constraint Sets](https://mlsanity.com/paper/2102.09005/) (2021)

## Abstract (arXiv)

Explanations are used in recommender systems for various reasons. Users have to be supported in making (high-quality) decisions more quickly. Developers of recommender systems want to convince users to purchase specific items. Users should better understand how the recommender system works and why a specific item has been recommended. Users should also develop a more in-depth understanding of the item domain. Consequently, explanations are designed in order to achieve specific goals such as increasing the transparency of a recommendation or increasing a user's trust in the recommender system. In this paper, we provide an overview of existing research related to explanations in recommender systems, and specifically discuss aspects relevant to group recommendation scenarios. In this context, we present different ways of explaining and visualizing recommendations determined on the basis of preference aggregation strategies.