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Tagsplanations: Explaining Recommendations Using Tags

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The objective of the authors is to come to a system that explains a recommendation. The idea is to use the concepts of tag relevance and tag preference. The experimental setting is movie recommendation. Recommendations are often item based, use-based or tag based. We are interested in such approaches where we see items as questions in a database and tags as keywords. If we can relate the user behaviour on questions, we can perhaps create a user dedicated recommendation o a keyword.
The paper focuses on creating a recommendation for an item (read movie) and a tag-based explanation (tagsplanation), where the choice of the (type of) tags is of importance. I feel that what we would like to do is not only item based, but yes individual user based, but we also would like to relate to keywords which are set by experts that according to them have relevance.

Scientific Paper

Machine Learning

Target Group:


Cite as:
Vig, J., Sen, S. & Riedl, J. (2008). Tagsplanations: explaining recommendations using tags. IUI '09: Proceedings of the 13th international conference on Intelligent user interfaces (p./pp. 47--56), New York, NY, USA: ACM. ISBN: 978-1-60558-168-2

Author of the review:
Eligius Hendrix
University of Malaga


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Pablo Guerrero-Garcia

This article was also included at Tintarev & Masthoff's survey chapter on design and evalutation of explanations for both the 1st (2011) and 2nd (2016) editions of the) Recommender Systems Handbook.