Abstract

Tag recommendation is helpful for the categorization and searching of online content. Existing tag recommendation methods can be divided into collaborative filtering methods and content based methods. In this paper, we put our focus on the content based tag recommendation due to its wider applicability. Our key observation is the tag-content co-occurrence, i.e., many tags have appeared multiple times in the corresponding content. Based on this observation, we propose a generative model (Tag2Word), where we generate the words based on the tag-word distribution as well as the tag itself. Experimental evaluations on real data sets demonstrate that the proposed method outperforms several existing methods in terms of recommendation accuracy, while enjoying linear scalability.

Original languageEnglish (US)
Title of host publicationCIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Pages2287-2292
Number of pages6
Volume24-28-October-2016
ISBN (Electronic)9781450340731
DOIs
StatePublished - Oct 24 2016
Event25th ACM International Conference on Information and Knowledge Management, CIKM 2016 - Indianapolis, United States
Duration: Oct 24 2016Oct 28 2016

Other

Other25th ACM International Conference on Information and Knowledge Management, CIKM 2016
CountryUnited States
CityIndianapolis
Period10/24/1610/28/16

Fingerprint

Tag
Evaluation
Scalability
Collaborative filtering

Keywords

  • Generative model
  • Tag recommendation
  • Tag-content co-occurrence

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Decision Sciences(all)

Cite this

Wu, Y., Yao, Y., Xu, F., Tong, H., & Lu, J. (2016). Tag2Word: Using tags to generate words for content based tag recommendation. In CIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management (Vol. 24-28-October-2016, pp. 2287-2292). Association for Computing Machinery. https://doi.org/10.1145/2983323.2983682

Tag2Word : Using tags to generate words for content based tag recommendation. / Wu, Yong; Yao, Yuan; Xu, Feng; Tong, Hanghang; Lu, Jian.

CIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management. Vol. 24-28-October-2016 Association for Computing Machinery, 2016. p. 2287-2292.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Wu, Y, Yao, Y, Xu, F, Tong, H & Lu, J 2016, Tag2Word: Using tags to generate words for content based tag recommendation. in CIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management. vol. 24-28-October-2016, Association for Computing Machinery, pp. 2287-2292, 25th ACM International Conference on Information and Knowledge Management, CIKM 2016, Indianapolis, United States, 10/24/16. https://doi.org/10.1145/2983323.2983682
Wu Y, Yao Y, Xu F, Tong H, Lu J. Tag2Word: Using tags to generate words for content based tag recommendation. In CIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management. Vol. 24-28-October-2016. Association for Computing Machinery. 2016. p. 2287-2292 https://doi.org/10.1145/2983323.2983682
Wu, Yong ; Yao, Yuan ; Xu, Feng ; Tong, Hanghang ; Lu, Jian. / Tag2Word : Using tags to generate words for content based tag recommendation. CIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management. Vol. 24-28-October-2016 Association for Computing Machinery, 2016. pp. 2287-2292
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abstract = "Tag recommendation is helpful for the categorization and searching of online content. Existing tag recommendation methods can be divided into collaborative filtering methods and content based methods. In this paper, we put our focus on the content based tag recommendation due to its wider applicability. Our key observation is the tag-content co-occurrence, i.e., many tags have appeared multiple times in the corresponding content. Based on this observation, we propose a generative model (Tag2Word), where we generate the words based on the tag-word distribution as well as the tag itself. Experimental evaluations on real data sets demonstrate that the proposed method outperforms several existing methods in terms of recommendation accuracy, while enjoying linear scalability.",
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