Interaction Tasks for Explainable Recommender Systems

Abstract
In the modern web experience, users interact with various types of recommender systems. In this literature study, we investigate the role of interaction in explainable recommender systems using 27 relevant papers from recommender systems, humancomputer interaction, and visualization fields. We structure interaction approaches into 1) the task, 2) the interaction intent, 3) the interaction technique, and 4) the interaction effect on explainable recommender systems. We present a preliminary interaction taxonomy for designers and developers to improve the interaction design of explainable recommender systems. Findings based on exploiting the descriptive power of the taxonomy emphasize the importance of interaction in creating effective and user-friendly explainable recommender systems.
Description

        
@inproceedings{
10.2312:evp.20231062
, booktitle = {
EuroVis 2023 - Posters
}, editor = {
Gillmann, Christina
and
Krone, Michael
and
Lenti, Simone
}, title = {{
Interaction Tasks for Explainable Recommender Systems
}}, author = {
Al-Hazwani, Ibrahim
and
Alahmadi, Turki
and
Wardatzky, Kathrin
and
Inel, Oana
and
El-Assady, Mennatallah
and
Bernard, Jürgen
}, year = {
2023
}, publisher = {
The Eurographics Association
}, ISBN = {
978-3-03868-220-2
}, DOI = {
10.2312/evp.20231062
} }
Citation