Proceedings ArticleDOI
Trust building with explanation interfaces
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Results of a significant-scale user study indicate that the organization-based explanation is highly effective in building users' trust in the recommendation interface, with the benefit of increasing users' intention to return to the agent and save cognitive effort.Abstract:
Based on our recent work on the development of a trust model for recommender agents and a qualitative survey, we explore the potential of building users' trust with explanation interfaces. We present the major results from the survey, which provided a roadmap identifying the most promising areas for investigating design issues for trust-inducing interfaces. We then describe a set of general principles derived from an in-depth examination of various design dimensions for constructing explanation interfaces, which most contribute to trust formation. We present results of a significant-scale user study, which indicate that the organization-based explanation is highly effective in building users' trust in the recommendation interface, with the benefit of increasing users' intention to return to the agent and save cognitive effort.read more
Citations
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Book ChapterDOI
Evaluating Recommendation Systems
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TL;DR: This paper discusses how to compare recommenders based on a set of properties that are relevant for the application, and focuses on comparative studies, where a few algorithms are compared using some evaluation metric, rather than absolute benchmarking of algorithms.
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A user-centric evaluation framework for recommender systems
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Recommender systems: from algorithms to user experience
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Explaining the user experience of recommender systems
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References
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Proceedings ArticleDOI
Explaining collaborative filtering recommendations
TL;DR: This paper presents experimental evidence that shows that providing explanations can improve the acceptance of ACF systems, and presents a model for explanations based on the user's conceptual model of the recommendation process.
Proceedings ArticleDOI
Trust in recommender systems
John O'Donovan,Barry Smyth +1 more
TL;DR: This paper proposes that the trustworthiness of users must be an important consideration in guiding recommendation and presents two computational models of trust and shows how they can be readily incorporated into standard collaborative filtering frameworks in a variety of ways.