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Agents Vs. Users: Visual Recommendation of Research Talks with Multiple Dimension of Relevance

Agents Vs. Users: Visual Recommendation of Research Talks with Multiple Dimension of Relevance Agents Vs. Users: Visual Recommendation of Research Talks with Multiple Dimension of Relevance KATRIEN VERBERT, KU Leuven DENIS PARRA, Pontificia Universidad Cat´ lica de Chile o PETER BRUSILOVSKY, University of Pittsburgh Several approaches have been researched to help people deal with abundance of information. An important feature pioneered by social tagging systems and later used in other kinds of social systems is the ability to explore different community relevance prospects by examining items bookmarked by a specific user or items associated by various users with a specific tag. A ranked list of recommended items offered by a specific recommender engine can be considered as another relevance prospect. The problem that we address is that existing personalized social systems do not allow their users to explore and combine multiple relevance prospects. Only one prospect can be explored at any given time--a list of recommended items, a list of items bookmarked by a specific user, or a list of items marked with a specific tag. In this article, we explore the notion of combining multiple relevance prospects as a way to increase effectiveness and trust. We used a visual approach to recommend articles at a conference by explicitly presenting multiple http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Interactive Intelligent Systems (TiiS) Association for Computing Machinery

Agents Vs. Users: Visual Recommendation of Research Talks with Multiple Dimension of Relevance

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2016 by ACM Inc.
ISSN
2160-6455
DOI
10.1145/2946794
Publisher site
See Article on Publisher Site

Abstract

Agents Vs. Users: Visual Recommendation of Research Talks with Multiple Dimension of Relevance KATRIEN VERBERT, KU Leuven DENIS PARRA, Pontificia Universidad Cat´ lica de Chile o PETER BRUSILOVSKY, University of Pittsburgh Several approaches have been researched to help people deal with abundance of information. An important feature pioneered by social tagging systems and later used in other kinds of social systems is the ability to explore different community relevance prospects by examining items bookmarked by a specific user or items associated by various users with a specific tag. A ranked list of recommended items offered by a specific recommender engine can be considered as another relevance prospect. The problem that we address is that existing personalized social systems do not allow their users to explore and combine multiple relevance prospects. Only one prospect can be explored at any given time--a list of recommended items, a list of items bookmarked by a specific user, or a list of items marked with a specific tag. In this article, we explore the notion of combining multiple relevance prospects as a way to increase effectiveness and trust. We used a visual approach to recommend articles at a conference by explicitly presenting multiple

Journal

ACM Transactions on Interactive Intelligent Systems (TiiS)Association for Computing Machinery

Published: Aug 3, 2016

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