The Effect of Explanations and Algorithmic Accuracy on Visual Recommender Systems of Artistic Images

Explore a 26-minute conference talk from the 24th International Conference on Intelligent User Interfaces that delves into the impact of explanations and algorithmic accuracy on visual recommender systems for artistic images. Investigate the findings of a study involving 121 users, which examined three interfaces with varying levels of explainability for artistic image recommendations. Discover how explanations enhance user satisfaction, perception of explainability, and relevance in the image domain. Learn about the interplay between recommendation algorithms and interfaces, comparing Deep Neural Networks (DNN) with high accuracy and Attractiveness Visual Features (AVF) with high transparency. Gain insights into the comprehensive model synthesizing various factors affecting user experience with explainable visual recommender systems for artistic images, based on the framework by Knijnenburg et al.