Explaining collaborative filtering recommendations
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
Empirical research in on-line trust: a review and critical assessment
International Journal of Human-Computer Studies - Special issue: Trust and technology
Trust-inspiring explanation interfaces for recommender systems
Knowledge-Based Systems
Explanations of recommendations
Proceedings of the 2007 ACM conference on Recommender systems
Knowledgeable Explanations for Recommender Systems
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Recommender Systems Handbook
Recommender Systems: An Introduction
Recommender Systems: An Introduction
A user-centric evaluation framework for recommender systems
Proceedings of the fifth ACM conference on Recommender systems
Persuasive Recommender Systems: Conceptual Background and Implications
Persuasive Recommender Systems: Conceptual Background and Implications
How should I explain? A comparison of different explanation types for recommender systems
International Journal of Human-Computer Studies
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Recommender Systems (RS) help online customers in identifying those items from a variety of choices that best match their presumed needs and preferences. In this context explanations summarize the reasons why a specific item is proposed and are capable of increasing the users' trust in the system's results. This paper presents results from an online experiment on a real-world platform indicating that explanations are an essential piece of functionality of a recommendation system, that significantly increases users' perception of the utility of a recommender system, the intention to use it repeatedly as well as the commitment to recommend it to others.