Article

P-TAG: large scale automatic generation of personalized annotation tags for the web

Authors:

Paul - Alexandru Chirita,

Stefania Costache,

Wolfgang Nejdl,

Siegfried HandschuhAuthors Info & Claims

WWW '07: Proceedings of the 16th international conference on World Wide Web

Pages 845 - 854

https://doi.org/10.1145/1242572.1242686

Published: 08 May 2007 Publication History

Abstract

The success of the Semantic Web depends on the availability of Web pages annotated with metadata. Free form metadata or tags, as used in social bookmarking and folksonomies, have become more and more popular and successful. Such tags are relevant keywords associated with or assigned to a piece of information (e.g., a Web page), describing the item and enabling keyword-based classification. In this paper we propose P-TAG, a method which automatically generates personalized tags for Web pages. Upon browsing a Web page, P-TAG produces keywords relevant both to its textual content, but also to the data residing on the surfer's Desktop, thus expressing a personalized viewpoint. Empirical evaluations with several algorithms pursuing this approach showed very promising results. We are therefore very confident that such a user oriented automatic tagging approach can provide large scale personalized metadata annotations as an important step towards realizing the Semantic Web.

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Cited By

Yang JGuo WLiu BYu YWang CLuo JKong LNiu DWen ZZhang ARangwala H(2022)TAG: Toward Accurate Social Media Content Tagging with a Concept GraphProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3534678.3539077(4332-4341)Online publication date: 14-Aug-2022
https://dl.acm.org/doi/10.1145/3534678.3539077
Ebrahimi FAsemi ANezarat AKo A(2021)Developing a mathematical model of the co-author recommender system using graph mining techniques and big data applicationsJournal of Big Data10.1186/s40537-021-00432-y8:1Online publication date: 6-Mar-2021
https://doi.org/10.1186/s40537-021-00432-y
Shokeen JRana C(2019)A study on features of social recommender systemsArtificial Intelligence Review10.1007/s10462-019-09684-wOnline publication date: 29-Jan-2019
https://doi.org/10.1007/s10462-019-09684-w
Show More Cited By

Index Terms

P-TAG: large scale automatic generation of personalized annotation tags for the web
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing systems and tools
2. Information systems
  1. Information retrieval
    1. Document representation
  2. World Wide Web

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Information & Contributors

Information

Published In

cover image ACM Conferences

WWW '07: Proceedings of the 16th international conference on World Wide Web

May 2007

1382 pages

ISBN:9781595936547

DOI:10.1145/1242572

General Chairs:
Carey Williamson
University of Calgary, Canada
,
Mary Ellen Zurko
IBM, USA
,
Program Chairs:
Peter Patel-Schneider
Bell Labs Research, USA
,
Prashant Shenoy
University of Massachusetts at Amherst, USA

Copyright © 2007 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]

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New York, NY, United States

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Published: 08 May 2007

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WWW'07: 16th International World Wide Web Conference

May 8 - 12, 2007

Alberta, Banff, Canada

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Yang JGuo WLiu BYu YWang CLuo JKong LNiu DWen ZZhang ARangwala H(2022)TAG: Toward Accurate Social Media Content Tagging with a Concept GraphProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3534678.3539077(4332-4341)Online publication date: 14-Aug-2022
https://dl.acm.org/doi/10.1145/3534678.3539077
Ebrahimi FAsemi ANezarat AKo A(2021)Developing a mathematical model of the co-author recommender system using graph mining techniques and big data applicationsJournal of Big Data10.1186/s40537-021-00432-y8:1Online publication date: 6-Mar-2021
https://doi.org/10.1186/s40537-021-00432-y
Shokeen JRana C(2019)A study on features of social recommender systemsArtificial Intelligence Review10.1007/s10462-019-09684-wOnline publication date: 29-Jan-2019
https://doi.org/10.1007/s10462-019-09684-w
Winter PSchulz MEngler T(2017)Finding Information Faster by Tracing My Colleagues' TrailsAnalyzing the Strategic Role of Social Networking in Firm Growth and Productivity10.4018/978-1-5225-0559-4.ch020(406-426)Online publication date: 2017
https://doi.org/10.4018/978-1-5225-0559-4.ch020
Yuan H(2017)A Method of Personalized Tag Prediction Based on Graph Structure2017 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C)10.1109/QRS-C.2017.71(390-395)Online publication date: Jul-2017
https://doi.org/10.1109/QRS-C.2017.71
Park JAmaro EMahajan DThwaites BEsmaeilzadeh H(2016)AxGamesACM SIGPLAN Notices10.1145/2954679.287237651:4(623-636)Online publication date: 25-Mar-2016
https://dl.acm.org/doi/10.1145/2954679.2872376
Patrignani NKavathatzopoulos I(2016)Cloud computingACM SIGCAS Computers and Society10.1145/2874239.287424945:3(68-72)Online publication date: 5-Jan-2016
https://dl.acm.org/doi/10.1145/2874239.2874249
Liang TChen LWu JBouguettaya A(2016)Exploiting Heterogeneous Information for Tag Recommendation in API Management2016 IEEE International Conference on Web Services (ICWS)10.1109/ICWS.2016.63(436-443)Online publication date: Jun-2016
https://doi.org/10.1109/ICWS.2016.63
Yang YLi YYue YWu ZShao W(2016)CUT: A Combined Approach for Tag Recommendation in Software Information SitesKnowledge Science, Engineering and Management10.1007/978-3-319-47650-6_47(599-612)Online publication date: 5-Oct-2016
https://doi.org/10.1007/978-3-319-47650-6_47
Godoy DCorbellini A(2016)Folksonomy-Based Recommender SystemsInternational Journal of Intelligent Systems10.1002/int.2175331:4(314-346)Online publication date: 1-Apr-2016
https://dl.acm.org/doi/10.1002/int.21753
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