Proceedings ArticleDOI
TasteWeights: a visual interactive hybrid recommender system
Svetlin Bostandjiev,John O'Donovan,Tobias Höllerer +2 more
- pp 35-42
TLDR
An evaluation of an interactive hybrid recommendation system that generates item predictions from multiple social and semantic web resources indicates that explanation and interaction with a visual representation of the hybrid system increase user satisfaction and relevance of predicted content.Abstract:
This paper presents an interactive hybrid recommendation system that generates item predictions from multiple social and semantic web resources, such as Wikipedia, Facebook, and Twitter. The system employs hybrid techniques from traditional recommender system literature, in addition to a novel interactive interface which serves to explain the recommendation process and elicit preferences from the end user. We present an evaluation that compares different interactive and non-interactive hybrid strategies for computing recommendations across diverse social and semantic web APIs. Results of the study indicate that explanation and interaction with a visual representation of the hybrid system increase user satisfaction and relevance of predicted content.read more
Citations
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Proceedings ArticleDOI
Principles of Explanatory Debugging to Personalize Interactive Machine Learning
TL;DR: An empirical evaluation shows that Explanatory Debugging increased participants' understanding of the learning system by 52% and allowed participants to correct its mistakes up to twice as efficiently as participants using a traditional learning system.
Journal ArticleDOI
Interactive recommender systems
TL;DR: An interactive visualization framework that combines recommendation with visualization techniques to support human-recommender interaction is presented and existing interactive recommender systems are analyzed along the dimensions of the framework.
Proceedings ArticleDOI
Visualizing recommendations to support exploration, transparency and controllability
TL;DR: It is investigated how information visualization can improve user understanding of the typically black-box rationale behind recommendations in order to increase their perceived relevance and meaning and to support exploration and user involvement in the recommendation process.
Journal ArticleDOI
A systematic review and taxonomy of explanations in decision support and recommender systems
TL;DR: In this paper, a comprehensive taxonomy of aspects to be considered when designing explanation facilities for current and future decision support systems for advice-giving systems is presented, which includes a variety of different facets, such as explanation objective, responsiveness, content and presentation.
Journal ArticleDOI
A systematic review and taxonomy of explanations in decision support and recommender systems
Ingrid Nunes,Dietmar Jannach +1 more
TL;DR: This work systematically review the literature on explanations in advice-giving systems, which includes recommender systems, and derives a novel comprehensive taxonomy of aspects to be considered when designing explanation facilities for current and future decision support systems.
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