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Social Information Access pp 441–479Cite as

Tag-Based Recommendation

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10100))

Abstract

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.

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Notes

  1. 1.

    This chapter focuses on how tags can be applied to improve the recommendation process. For more information about the paradigm of social tagging, we refer the reader to Mathes [71] and Hammond et al. [46]. For other applications of tags to information access, we refer the reader to Chapters 1 [15], 6 [27], and 9 [75] in this book.

  2. 2.

    http://www.last.fm.

  3. 3.

    http://www.movielens.org.

  4. 4.

    http://www.librarything.com.

  5. 5.

    http://www.delicious.com.

  6. 6.

    http://www.citeulike.org.

  7. 7.

    The active user is the user the system is currently generating recommendations for.

  8. 8.

    http://www.bibsonomy.org.

  9. 9.

    The Netflix Prize was an open competition organized by Netflix which ran from 2006 to 2009. The aim was to develop the best recommendation algorithm to predict users’ ratings for movies offered by Netflix. See http://www.netflixprize.com for more information.

  10. 10.

    http://www.imdb.com.

  11. 11.

    See also Chap. 9 [75] in this book for a more detailed description of FolkRank.

  12. 12.

    While there are multiple versions of the MovieLens data set available for download at http://grouplens.org/datasets/movielens/, only the MovieLens 10M data set contains tagging information. This is the data set we refer to as MovieLens in this book chapter.

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  1. Department of Communication and Psychology, Aalborg University Copenhagen, Copenhagen, Denmark

    Toine Bogers

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  1. University of Pittsburgh, Pittsburgh, Pennsylvania, USA

    Peter Brusilovsky

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    Daqing He

Appendix A: Data Sets for Tag-Based Recommendation

Appendix A: Data Sets for Tag-Based Recommendation

Below is an overview of the most commonly used publicly available recommendation data sets that include tagging information, organized by source.

BibSonomy

Benz et al. [6] make several dumps of the BibSonomy system available at http://www.kde.cs.uni-kassel.de/bibsonomy/dumps/.

CiteULike

CiteULike makes dumps of their folksonomy (user-item-tag relations with timestamps) available on their website at http://www.citeulike.org/faq/data.adp.

Delicious

Wetzker et al. [109] have made their Delicious data sets publicly available at http://www.dai-labor.de/en/irml/datasets/delicious/.

Flickr

Cantador et al. [19] have made their Flickr data set publicly available at http://mir.dcs.gla.ac.uk/resources/.

Last.FM

Konstas et al. [60] have made a Last.FM data set available at http://mir.dcs.gla.ac.uk/resources/.

LibraryThing

Two different tagging data sets based on LibraryThing have been made available. Clements et al. [22] made their LibraryThing data set available at http://www.macle.nl/tud/LT/. A recommendation data set including tagging information from LibraryThing has been made available as part of the Social Book Search track at CLEF [61]. See http://social-book-search.humanities.uva.nl/ for more information.

MovieLens

The GroupLens research group have a long history of making data sets from their movie recommender system available. The latest two versions, MovieLens 10M and MovieLens 20M, also contain tagging information, although only the former has been used for evaluation of tag-based recommender systems so far. More information on how to obtain these data sets can be found at http://grouplens.org/datasets/movielens/.

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Bogers, T. (2018). Tag-Based Recommendation. In: Brusilovsky, P., He, D. (eds) Social Information Access. Lecture Notes in Computer Science(), vol 10100. Springer, Cham. https://doi.org/10.1007/978-3-319-90092-6_12

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