How should I explain? A comparison of different explanation types for recommender systems

By Fatih Gedikli, Dietmar Jannach, Mouzhi Ge. Int. J. Hum. Comput. Stud., 2014

343 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/d0cddb1bcf2f2dd2671ef1971d608bd742064f95

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In Artificial Intelligence

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Citations in the corpus

A Survey on Conversational Recommender Systems (2020, background): “ender systems is widely recognized [50, 101, 136]. Explanations can increase the system’s perceived transparency, user trust and satisfaction, and they can help users make faster and better decisions [44]. However, according to our survey, few papers so far have studied the explanation issue specifi”
Using Explainability for Constrained Matrix Factorization (2017, methodology): “[6] used the same user-based approach as their base recommender system module and compared 10 di erent existing explanation types and their impact on the perceived level of transparency and hence the satisfaction of the user.”
ReEL: Review Aware Explanation of Location Recommendation (2018, result): “Contrary to that, some of the studies [17, 18, 35, 36, 40, 41, 43, 57] have already claimed the user persuasiveness due to explainability in real-world systems.”
Evaluating Recommender Systems with User Experiments (2015, background): “Another challenge for recommender systems is to explain their recommendations (see [40, 41, 110] for an overview).”
Scalable and Interpretable Product Recommendations via Overlapping Co-Clustering (2016, methodology): “Generic methodologies for communicating explanations to the user have been studied in [42], [11], [12].”
A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems (2018, background): “Interview and Self-report ([25, 62, 124, 125]) Likert-scale Questionnaire ([39, 62, 112, 124, 125]) Expert Case Study ([95, 106, 128, 130, 193])”
A systematic review and taxonomy of explanations in decision support and recommender systems (2017, background): “(Non-)personalised tag-based (popularity, neighbours) (Gedikli et al. 2014)”
Explanation-based Ranking in Opinionated Recommender Systems (2018, background): “Recently, explanations have been become an interesting area of research for recommender systems [6] where they have been shown to significantly increase how users perceive the utility of recommender systems [14].”
Explanations as Mechanisms for Supporting Algorithmic Transparency (2018, background): “Explanations improve system usability and overall performance, and promote more positive user perceptions and acceptance of the system [10, 24, 21, 37].”
Transparent, Scrutable and Explainable User Models for Personalized Recommendation (2019, background): “Gedikli et al. [17] find content-based tag cloud explanations effective and particularly well accepted by users.”
Make it personal: A social explanation system applied to group recommendations (2017, background): “…different ways in which explanations can be evaluated, how they differ with different explanation criteria and how, based on previous research (Gedikli et al., 2014), we evaluate each of the objectives that we have focused on: - Persuasiveness, is mainly understood as the ability of an…”