research-article

Learning to recognize valuable tags

Authors:

John RiedlAuthors Info & Claims

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

Pages 87 - 96

https://doi.org/10.1145/1502650.1502666

Published: 08 February 2009 Publication History

Abstract

Many websites use tags as a mechanism for improving item metadata through collective user effort. Users of tagging systems often apply far more tags to an item than a system can display. These tags can range in quality from tags that capture a key facet of an item, to those that are subjective, irrelevant, or misleading. In this paper we explore tag selection algorithms that choose the tags that sites display. Based on 225,000 ratings and survey responses, we conduct offline analyses of 21 tag selection algorithms. We select the three best performing algorithms from our offline analysis, and deploy them live on the MovieLens website to 5,695 users for three months. Based on our results, we offer tagging system designers advice about tag selection algorithms.

References

[1]

M. Ames and M. Naaman. Why we tag: motivations for annotation in mobile and online media. In CHI '07: Proceedings of the SIGCHI conference on Human factors in computing systems, pages 971--980, New York, NY, USA, 2007. ACM.

Digital Library

[2]

A. Arnt and S. Zilberstein. Learning to perform moderation in online forums. Web Intelligence, 2003. WI 2003. Proceedings. IEEE/WIC International Conference on, pages 637--641, 2003.

Digital Library

[3]

M. Balabanovic and Y. Shoham. Content-based, collaborative recommendation. Communications of the ACM, 40(3):66--72, 1997.

Digital Library

[4]

J. Besag and P. J. Green. Spatial statistics and bayesian computation. Journal of the Royal Statistical Society. Series B (Methodological), 55(1):25--37, 1993.

[5]

V. Bouthors and O. Dedieu. Pharos, a collaborative infrastructure for web knowledge sharing. In ECDL, pages 215--233, London, UK, 1999. Springer-Verlag.

Digital Library

[6]

B. P. Carlin and T. A. Louis. Bayes and empirical bayes methods for data analysis. Statistics and Computing, 7(2):153--154, 1997.

Digital Library

[7]

C. Cattuto, V. Loreto, and L. Pietronero. Semiotic dynamics in online social communities. In The European Physical Journal C (accepted). Springer-Verlag, 2006.

[8]

D. Cosley, D. Frankowski, S. Kiesler, L. Terveen, and J. Riedl. How oversight improves member-maintained communities. In Proceedings of CHI 2005, pages 11--20, New York, NY, 2005. ACM Press.

Digital Library

[9]

D. Cosley, D. Frankowski, L. Terveen, and J. Riedl. Using intelligent task routing and contribution review to help communities build artifacts of lasting value. In Proceedings of ACM CHI, Montreal, CA, 2006.

Digital Library

[10]

A. Gelman, J. B. Carlin, H. S. Stern, and D. B. Rubin. Bayesian Data Analysis, Second Edition. Chapman & Hall/CRC, July 2003.

[11]

N. Glance, D. Arregui, and M. Dardenne. Knowledge pump: Supporting the flow and use of knowledge. In Information Technology for Knowledge Management. Springer-Verlag, 1998.

[12]

S. Golder and B. A. Huberman. The structure of collaborative tagging systems. Journal of Information Science (accepted), 2006.

Digital Library

[13]

J. Herlocker, J. Konstan, L. Terveen, and J. Riedl. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems, 22(1):5--53, Jan. 2004.

Digital Library

[14]

T. Joachims. Training linear svms in linear time. In KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 217--226, New York, NY, USA, 2006. ACM.

Digital Library

[15]

C. Lampe and P. Resnick. Slash (dot) and Burn: Distributed Moderation in a Large Online Conversation Space. Proceedings of SIGCHI, pages 543--550.

Digital Library

[16]

G. MacGregor and E. McCulloch. Collaborative tagging as a knowledge organisation and resource discovery tool. Library View (accepted), 55(5), 2006.

[17]

M. M. Markus Weimer, Iryna Gurevych. Automatically assesing the post quality in online discussions on software. In Proceedings of the Association for Computational Linguistics, 2007.

Digital Library

[18]

D. Millen, J. Feinberg, and B. Kerr. Social bookmarking in the enterprise. ACM Queue, 3(9):28--35, 2005.

Digital Library

[19]

S. Sen, F. M. Harper, A. LaPitz, and J. Riedl. The quest for quality tags. In GROUP '07: Proceedings of the 2007 international ACM conference on Supporting group work, pages 361--370, New York, NY, USA, 2007. ACM.

Digital Library

[20]

S. Sen, S. K. Lam, A. M. Rashid, D. Cosley, D. Frankowski, J. Osterhouse, F. M. Harper, and J. Riedl. tagging, communities, vocabulary, evolution. In Proceedings of the ACM 2006 Conference on CSCW, Ban, Alberta, Canada, 2006.

Digital Library

Cited By

Deshpande AEmmanuel M(2022)Context Dependency with User Action in Recommendation SystemSmart Intelligent Computing and Applications, Volume 110.1007/978-981-16-9669-5_43(471-479)Online publication date: 19-Apr-2022
https://doi.org/10.1007/978-981-16-9669-5_43
Yao YHarper FPera SEkstrand MAmatriain XO'Donovan J(2018)Judging similarityProceedings of the 12th ACM Conference on Recommender Systems10.1145/3240323.3240351(288-296)Online publication date: 27-Sep-2018
https://dl.acm.org/doi/10.1145/3240323.3240351
Aggarwal CAggarwal C(2016)Social and Trust-Centric Recommender SystemsRecommender Systems10.1007/978-3-319-29659-3_11(345-384)Online publication date: 29-Mar-2016
https://doi.org/10.1007/978-3-319-29659-3_11
Show More Cited By

Index Terms

Learning to recognize valuable tags
1. Human-centered computing
  1. Collaborative and social computing
  2. Human computer interaction (HCI)

Recommendations

The quest for quality tags
GROUP '07: Proceedings of the 2007 ACM International Conference on Supporting Group Work

Many online communities use tags - community selected words or phrases - to help people find what they desire. The quality of tags varies widely, from tags that capture akey dimension of an entity to those that are profane, useless, or unintelligible. ...
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 ...
Visualizing Tags with Spatiotemporal References
IV '11: Proceedings of the 2011 15th International Conference on Information Visualisation

Nowadays, a great amount of data is created and distributed on the Internet. Tagging has become common practice to structure these data for easy access. Often the data and the associated tags contain spatial and temporal information. In this paper, we ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

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.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 February 2009

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

IUI09

Sponsor:

IUI09: 14th International Conference on Intelligent User Interfaces

February 8 - 11, 2009

Florida, Sanibel Island, USA

Acceptance Rates

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

Upcoming Conference

IUI '25

Sponsor:
sigai
sigai

30th International Conference on Intelligent User Interfaces

March 24 - 27, 2025

Cagliari , Italy

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

16
Total Citations
View Citations
645
Total Downloads

Downloads (Last 12 months)4
Downloads (Last 6 weeks)0

Reflects downloads up to 19 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Deshpande AEmmanuel M(2022)Context Dependency with User Action in Recommendation SystemSmart Intelligent Computing and Applications, Volume 110.1007/978-981-16-9669-5_43(471-479)Online publication date: 19-Apr-2022
https://doi.org/10.1007/978-981-16-9669-5_43
Yao YHarper FPera SEkstrand MAmatriain XO'Donovan J(2018)Judging similarityProceedings of the 12th ACM Conference on Recommender Systems10.1145/3240323.3240351(288-296)Online publication date: 27-Sep-2018
https://dl.acm.org/doi/10.1145/3240323.3240351
Aggarwal CAggarwal C(2016)Social and Trust-Centric Recommender SystemsRecommender Systems10.1007/978-3-319-29659-3_11(345-384)Online publication date: 29-Mar-2016
https://doi.org/10.1007/978-3-319-29659-3_11
Harper FKonstan J(2015)The MovieLens DatasetsACM Transactions on Interactive Intelligent Systems10.1145/28278725:4(1-19)Online publication date: 22-Dec-2015
https://dl.acm.org/doi/10.1145/2827872
Gedikli FJannach D(2013)Improving recommendation accuracy based on item-specific tag preferencesACM Transactions on Intelligent Systems and Technology10.1145/2414425.24144364:1(1-19)Online publication date: 1-Feb-2013
https://dl.acm.org/doi/10.1145/2414425.2414436
Nguyen TRiedl J(2013)Predicting Users’ Preference from Tag RelevanceUser Modeling, Adaptation, and Personalization10.1007/978-3-642-38844-6_23(274-280)Online publication date: 2013
https://doi.org/10.1007/978-3-642-38844-6_23
Liu JWang WChen ZDu XQi Q(2012)A novel user-based collaborative filtering method by inferring tag ratingsACM SIGAPP Applied Computing Review10.1145/2432546.243255012:4(48-57)Online publication date: 1-Dec-2012
https://dl.acm.org/doi/10.1145/2432546.2432550
Wang WChen ZLiu JQi QZhao ZLong BChang YLi H(2012)User-based collaborative filtering on cross domain by tag transfer learningProceedings of the 1st International Workshop on Cross Domain Knowledge Discovery in Web and Social Network Mining10.1145/2351333.2351335(10-17)Online publication date: 12-Aug-2012
https://dl.acm.org/doi/10.1145/2351333.2351335
Qi QChen ZLiu JHui CWu QOssowski SLecca P(2012)Using inferred tag ratings to improve user-based collaborative filteringProceedings of the 27th Annual ACM Symposium on Applied Computing10.1145/2245276.2232110(2008-2013)Online publication date: 26-Mar-2012
https://dl.acm.org/doi/10.1145/2245276.2232110
Kashoob SCaverlee J(2012)Temporal dynamics of communities in social bookmarking systemsSocial Network Analysis and Mining10.1007/s13278-012-0054-z2:4(387-404)Online publication date: 1-Mar-2012
https://doi.org/10.1007/s13278-012-0054-z
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents