A Taxonomy for Generating Explanations in Recommender Systems

In recommender systems, explanations serve as an additional type of information that can help users to better understand the system's output and promote objectives such as trust, confidence in decision making, or utility. This article proposes a taxonomy to categorize and review the research in...

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Bibliographic Details
Published inThe AI magazine Vol. 32; no. 3; pp. 90 - 98
Main Authors Friedrich, Gerhard, Zanker, Markus
Format Journal Article
LanguageEnglish
Published Menlo Park, CA American Association for Artificial Intelligence 22.09.2011
John Wiley & Sons, Inc
Subjects
Algorithms
Applied sciences
Artificial intelligence
Classification
Classification (Library Science)
Collaboration
Computer science; control theory; systems
Computer systems and distributed systems. User interface
Controlled vocabularies
Data mining
Decision making
Design
Exact sciences and technology
Filtering systems
Learning and adaptive systems
Objectives
Recommendation systems
Recommender systems
Software
Studies
Syntax
Taxonomy
United States
Confidence
Decision making
Taxonomy
News
User assistance
Artificial intelligence
Recommendation
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Summary:In recommender systems, explanations serve as an additional type of information that can help users to better understand the system's output and promote objectives such as trust, confidence in decision making, or utility. This article proposes a taxonomy to categorize and review the research in the area of explanations. It provides a unified view on the different recommendation paradigms, allowing similarities and differences to be clearly identified. Finally, the authors present their view on open research issues and opportunities for future work on this topic.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0738-4602
2371-9621
DOI:10.1609/aimag.v32i3.2365