research-article

Exploiting relevance, novelty and diversity in tag recommendation

Authors:

Fabiano Muniz Belém,

Eder Ferreira Martins,

Jussara Marques Almeida,

Marcos André GonçalvesAuthors Info & Claims

WebMedia '12: Proceedings of the 18th Brazilian symposium on Multimedia and the web

Pages 297 - 300

https://doi.org/10.1145/2382636.2382699

Published: 15 October 2012 Publication History

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Abstract

Tag recommendation methods have mostly focused on maximizing relevance, but other aspects may be as important for recommendation usefulness. We here define novelty and diversity for tag recommendation, and propose two new recommendation strategies that consider these aspects jointly with relevance. We evaluate the proposed strategies using real datasets from 3 popular Web 2.0 applications, achieving gains over the state-of-the-art of up to 21% in relevance, 45% in novelty and 2.5\% in diversity.

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Cited By

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Chakraborty JVerma V(2017)Diversification in Tag Recommendation System Using Binomial FrameworkInformation and Communication Technology for Sustainable Development10.1007/978-981-10-3932-4_44(423-430)Online publication date: 8-Nov-2017
https://doi.org/10.1007/978-981-10-3932-4_44
Chakraborty JVerma V(2016)A survey of diversification techniques in Recommendation Systems2016 International Conference on Data Mining and Advanced Computing (SAPIENCE)10.1109/SAPIENCE.2016.7684161(35-40)Online publication date: Mar-2016
https://doi.org/10.1109/SAPIENCE.2016.7684161

Index Terms

Exploiting relevance, novelty and diversity in tag recommendation
1. Information systems
  1. World Wide Web
    1. Web applications
    2. Web services

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Information

Published In

WebMedia '12: Proceedings of the 18th Brazilian symposium on Multimedia and the web

October 2012

426 pages

ISBN:9781450317061

DOI:10.1145/2382636

General Chairs:
Graça Bressan
University of São Paulo, Brazil
,
Regina Melo Silveira
University of São Paulo, Brazil
,
Program Chairs:
Ethan V. Munson
University of Wisconsin-Milwaukee, USA
,
André Santanchà
State University of Campinas, Brazil
,
Maria da Graèa Campos Pimentel
University of São Paulo, Brazil

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 October 2012

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WebMedia '12

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WebMedia '12: Brazilian Symposium on Multimedia and the Web

October 15 - 18, 2012

São Paulo/SP, Brazil

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Overall Acceptance Rate 270 of 873 submissions, 31%

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Cited By

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Chakraborty JVerma V(2017)Diversification in Tag Recommendation System Using Binomial FrameworkInformation and Communication Technology for Sustainable Development10.1007/978-981-10-3932-4_44(423-430)Online publication date: 8-Nov-2017
https://doi.org/10.1007/978-981-10-3932-4_44
Chakraborty JVerma V(2016)A survey of diversification techniques in Recommendation Systems2016 International Conference on Data Mining and Advanced Computing (SAPIENCE)10.1109/SAPIENCE.2016.7684161(35-40)Online publication date: Mar-2016
https://doi.org/10.1109/SAPIENCE.2016.7684161

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