Tag-Based Recommendation

Research output: Contribution to book/anthology/report/conference proceedingBook chapterResearchpeer-review

29 Citations (Scopus)


Social tagging is an information classification paradigm where the users themselves are given the power to describe and categorize content for their own purposes using tags. The popularity of social tagging, and the ease with which such tags can be generated, assigned, and collected, has sparked significant research interest in tags and their possible applications. One such application is tag-based recommendation: generating better recommendations by incorporating tags into the recommendation process. This chapter provides an overview of the state-of-the-art approaches to tag-based item recommendation, organised by the class of recommendation algorithms that is augmented with tags, such as collaborative filtering, dimensionality reduction, graph-based recommendation, content-based filtering, machine learning, and hybrid recommendation. The chapter also offers an overview of the most important methods for recommending which tags to apply to content. Finally, the chapter discusses the open research problems in tag-based recommendation and what would be needed to address them.
Original languageEnglish
Title of host publicationSocial Information Access
EditorsPeter Brusilovsky, Daqing He
Number of pages39
Publication date1 Jan 2018
ISBN (Print)978-3-319-90091-9
ISBN (Electronic)978-3-319-90092-6
Publication statusPublished - 1 Jan 2018
SeriesLecture Notes in Computer Science


  • social tagging
  • social bookmarking
  • collaborative filtering
  • recommender systems
  • content-based recommendation
  • machine learning
  • hybrid recommendation
  • tag recommendation
  • tag-based recommendation


Dive into the research topics of 'Tag-Based Recommendation'. Together they form a unique fingerprint.

Cite this