research-article

Tagsplanations: explaining recommendations using tags

Authors:

John RiedlAuthors Info & Claims

IUI '09: Proceedings of the 14th international conference on Intelligent user interfaces

Pages 47 - 56

https://doi.org/10.1145/1502650.1502661

Published: 08 February 2009 Publication History

Abstract

While recommender systems tell users what items they might like, explanations of recommendations reveal why they might like them. Explanations provide many benefits, from improving user satisfaction to helping users make better decisions. This paper introduces tagsplanations, which are explanations based on community tags. Tagsplanations have two key components: tag relevance, the degree to which a tag describes an item, and tag preference, the user's sentiment toward a tag. We develop novel algorithms for estimating tag relevance and tag preference, and we conduct a user study exploring the roles of tag relevance and tag preference in promoting effective tagsplanations. We also examine which types of tags are most useful for tagsplanations.

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Cited By

Li DLiu ZZhang QKou YLiu TQu H(2025)Integrating User Sentiment and Behavior for Explainable RecommendationWeb and Big Data. APWeb-WAIM 2024 International Workshops10.1007/978-981-96-0055-7_12(135-148)Online publication date: 31-Jan-2025
https://doi.org/10.1007/978-981-96-0055-7_12
Chatti MGuesmi MMuslim A(2024)Visualization for Recommendation Explainability: A Survey and New PerspectivesACM Transactions on Interactive Intelligent Systems10.1145/367227614:3(1-40)Online publication date: 11-Jun-2024
https://dl.acm.org/doi/10.1145/3672276
Schott KPapenmeier AHienert DKern D(2024)What Did I Say Again? Relating User Needs to Search Outcomes in Conversational CommerceProceedings of Mensch und Computer 202410.1145/3670653.3670680(129-139)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1145/3670653.3670680
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Index Terms

Tagsplanations: explaining recommendations using tags
1. Human-centered computing
  1. Collaborative and social computing

Recommendations

Tagommenders: connecting users to items through tags
WWW '09: Proceedings of the 18th international conference on World wide web

Tagging has emerged as a powerful mechanism that enables users to find, organize, and understand online entities. Recommender systems similarly enable users to efficiently navigate vast collections of items. Algorithms combining tags with recommenders ...
Intelligent tagging interfaces: beyond folksonomy
UIST '10: Adjunct proceedings of the 23nd annual ACM symposium on User interface software and technology

This paper summarizes our work on using tags to broaden the dialog between a recommender system and its users. We present two tagging applications that enrich this dialog: tagsplanations are tag-based explanations of recommendations provided by a system ...
Improving recommendation accuracy based on item-specific tag preferences
Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context

In recent years, different proposals have been made to exploit Social Web tagging information to build more effective recommender systems. The tagging data, for example, were used to identify similar users or were viewed as additional information about ...

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Published In

cover image ACM Conferences

IUI '09: Proceedings of the 14th international conference on Intelligent user interfaces

February 2009

522 pages

ISBN:9781605581682

DOI:10.1145/1502650

General Chairs:
Cristina Conati
University of British Columbia, Canada
,
Mathias Bauer
mineway GmbH, Germany
,
Program Chairs:
Nuria Oliver
Telefonica Research, Spain
,
Dan Weld
University of Washington, USA

Copyright © 2009 ACM.

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Publication History

Published: 08 February 2009

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IUI09

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IUI09: 14th International Conference on Intelligent User Interfaces

February 8 - 11, 2009

Florida, Sanibel Island, USA

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Overall Acceptance Rate 746 of 2,811 submissions, 27%

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March 24 - 27, 2025

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Cited By

Li DLiu ZZhang QKou YLiu TQu H(2025)Integrating User Sentiment and Behavior for Explainable RecommendationWeb and Big Data. APWeb-WAIM 2024 International Workshops10.1007/978-981-96-0055-7_12(135-148)Online publication date: 31-Jan-2025
https://doi.org/10.1007/978-981-96-0055-7_12
Chatti MGuesmi MMuslim A(2024)Visualization for Recommendation Explainability: A Survey and New PerspectivesACM Transactions on Interactive Intelligent Systems10.1145/367227614:3(1-40)Online publication date: 11-Jun-2024
https://dl.acm.org/doi/10.1145/3672276
Schott KPapenmeier AHienert DKern D(2024)What Did I Say Again? Relating User Needs to Search Outcomes in Conversational CommerceProceedings of Mensch und Computer 202410.1145/3670653.3670680(129-139)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1145/3670653.3670680
Li SPrasad Majumder BMcAuley J(2024)Self-Supervised Bot Play for Transcript-Free Conversational Critiquing with RationalesACM Transactions on Recommender Systems10.1145/36655023:1(1-20)Online publication date: 2-Aug-2024
https://dl.acm.org/doi/10.1145/3665502
Zhuang HZhang WChen WYang JSheng Q(2024)Improving Faithfulness and Factuality with Contrastive Learning in Explainable RecommendationACM Transactions on Intelligent Systems and Technology10.1145/365398416:1(1-23)Online publication date: 26-Dec-2024
https://dl.acm.org/doi/10.1145/3653984
Ge YLiu SFu ZTan JLi ZXu SLi YXian YZhang Y(2024)A Survey on Trustworthy Recommender SystemsACM Transactions on Recommender Systems10.1145/36528913:2(1-68)Online publication date: 13-Apr-2024
https://dl.acm.org/doi/10.1145/3652891
Shashaani S(2024)Explainability in Music Recommender SystemProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688028(1395-1401)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688028
Al-Hazwani ILuo TInel ORicci FEl-Assady MBernard JBoratto LGena CMarras MGermanakos PPopescus E(2024)ScrollyPOI: A Narrative-Driven Interactive Recommender System for Points-of-Interest Exploration and ExplainabilityAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3665183(292-304)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3665183
Kotkov DMedlar ALiu YGlowacka DHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)On the Negative Perception of Cross-domain Recommendations and ExplanationsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657735(2102-2113)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657735
Juneja PZhang WSmith-Renner ALamba HTetreault JJaimes A(2024)Dissecting users' needs for search result explanationsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642059(1-17)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642059
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