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Personalized tag suggestion for flickr

Authors:

Nikhil Garg,

Ingmar WeberAuthors Info & Claims

WWW '08: Proceedings of the 17th international conference on World Wide Web

Pages 1063 - 1064

https://doi.org/10.1145/1367497.1367657

Published: 21 April 2008 Publication History

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Abstract

We present a system for personalized tag suggestion for Flickr: While the user is entering/selecting new tags for a particular picture, the system is suggesting related tags to her, based on the tags that she or other people have used in the past along with (some of) the tags already entered. The suggested tags are dynamically updated with every additional tag entered/selected. We describe three algorithms which can be applied to this problem. In experiments, our best-performing method yields an improvement in precision of 10-15% over a baseline method very similar to the system currently used by Flickr. Our system is accessible at http://ltaa5.epfl.ch/flickr-tags/.

To the best of our knowledge, this is the first study on tag suggestion in a setting where (i) no full text information is available, such as for blogs, (ii) no item has been tagged by more than one person, such as for social bookmarking sites, and (iii) suggestions are dynamically updated, requiring efficient yet effective algorithms.

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Cited By

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Bogers T(2018)Tag-Based RecommendationSocial Information Access10.1007/978-3-319-90092-6_12(441-479)Online publication date: 3-May-2018
https://doi.org/10.1007/978-3-319-90092-6_12
Spyrou EMylonas P(2016)A survey on Flickr multimedia research challengesEngineering Applications of Artificial Intelligence10.1016/j.engappai.2016.01.00651:C(71-91)Online publication date: 1-May-2016
https://dl.acm.org/doi/10.1016/j.engappai.2016.01.006
Liang YCaverlee JCao C(2015)A Noise-Filtering Approach for Spatio-temporal Event Detection in Social MediaAdvances in Information Retrieval10.1007/978-3-319-16354-3_25(233-244)Online publication date: 2015
https://doi.org/10.1007/978-3-319-16354-3_25
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Index Terms

Personalized tag suggestion for flickr
1. Human-centered computing
  1. Human computer interaction (HCI)
  2. Interaction design
    1. Interaction design theory, concepts and paradigms

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Information & Contributors

Information

Published In

WWW '08: Proceedings of the 17th international conference on World Wide Web

April 2008

1326 pages

ISBN:9781605580852

DOI:10.1145/1367497

General Chairs:
Jinpeng Huai
Beihang University, China
,
Robin Chen
AT&T Labs, USA
,
Hsiao-Wuen Hon
Microsoft Research Asia, China
,
Yunhao Liu
HK University of Science and Technology, Hong Kong
,
Program Chairs:
Wei-Ying Ma
Microsoft Research Asia, China
,
Andrew Tomkins
Yahoo! Research, USA
,
Xiaodong Zhang
The Ohio State University, USA

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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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 April 2008

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WWW '08

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WWW '08: The 17th International World Wide Web Conference

April 21 - 25, 2008

Beijing, China

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

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Cited By

View all

Bogers T(2018)Tag-Based RecommendationSocial Information Access10.1007/978-3-319-90092-6_12(441-479)Online publication date: 3-May-2018
https://doi.org/10.1007/978-3-319-90092-6_12
Spyrou EMylonas P(2016)A survey on Flickr multimedia research challengesEngineering Applications of Artificial Intelligence10.1016/j.engappai.2016.01.00651:C(71-91)Online publication date: 1-May-2016
https://dl.acm.org/doi/10.1016/j.engappai.2016.01.006
Liang YCaverlee JCao C(2015)A Noise-Filtering Approach for Spatio-temporal Event Detection in Social MediaAdvances in Information Retrieval10.1007/978-3-319-16354-3_25(233-244)Online publication date: 2015
https://doi.org/10.1007/978-3-319-16354-3_25
Liang BLiu YZhang MMa SRu LZhang K(2014)Tag Expansion Using Friendship Information: Services for Picking-a-crowd for CrowdsourcingSocial Media Processing10.1007/978-3-662-45558-6_3(25-43)Online publication date: 2014
https://doi.org/10.1007/978-3-662-45558-6_3
Spyrou EMylonas P(2014)A Survey of Geo-tagged Multimedia Content Analysis within FlickrProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications10.1007/978-3-662-44722-2_14(126-135)Online publication date: 2014
https://doi.org/10.1007/978-3-662-44722-2_14
Cagliero LFiori A(2013)Dynamic Social Network MiningData Mining in Dynamic Social Networks and Fuzzy Systems10.4018/978-1-4666-4213-3.ch007(122-144)Online publication date: 2013
https://doi.org/10.4018/978-1-4666-4213-3.ch007
Cagliero LFiori A(2013)Knowledge Discovery from Online CommunitiesData Mining10.4018/978-1-4666-2455-9.ch063(1230-1252)Online publication date: 2013
https://doi.org/10.4018/978-1-4666-2455-9.ch063
Yabo Ni Miao Zheng Jiajun Bu Chun Chen Dazhou Wang (2013)Personalized automatic image annotation based on reinforcement learning2013 IEEE International Conference on Multimedia and Expo (ICME)10.1109/ICME.2013.6607456(1-6)Online publication date: Jul-2013
https://doi.org/10.1109/ICME.2013.6607456
Zhang XZhao XLi ZXia JJain RChao W(2013)Social image tagging using graph-based reinforcement on multi-type interrelated objectsSignal Processing10.1016/j.sigpro.2012.05.02193:8(2178-2189)Online publication date: 1-Aug-2013
https://dl.acm.org/doi/10.1016/j.sigpro.2012.05.021
Chen XShin H(2013)Tag recommendation by machine learning with textual and social featuresJournal of Intelligent Information Systems10.1007/s10844-012-0200-040:2(261-282)Online publication date: 1-Apr-2013
https://dl.acm.org/doi/10.1007/s10844-012-0200-0
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