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Recommendations Based on Social Links

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

Abstract

The goal of this chapter is to give an overview of recent works on the development of social link-based recommender systems and to offer insights on related issues, as well as future directions for research. Among several kinds of social recommendations, this chapter focuses on recommendations, which are based on users’ self-defined (i.e., explicit) social links and suggest items, rather than people of interest. The chapter starts by reviewing the needs for social link-based recommendations and studies that explain the viability of social networks as useful information sources. Following that, the core part of the chapter dissects and examines modern research on social link-based recommendations along several dimensions. It concludes with a discussion of several important issues and future directions for social link-based recommendation research.

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Notes

  1. 1.

    Epinions.com aims to review a wide range of products from digital gadgets, appliances, sports gears, toys, movies, books, songs and more. None of the studies using Epinions.com datasets clearly stated the product category of the target items; hence we classified the target items as general products.

  2. 2.

    http://www.trustlet.org/wiki/Epinions_dataset.

  3. 3.

    http://alchemy.cs.washington.edu/data/epinions/.

  4. 4.

    http://www.netflixprize.com.

  5. 5.

    http://www.librec.net/datasets.html.

  6. 6.

    http://dl.dropbox.com/u/17517913/Douban.zip.

  7. 7.

    http://www.dtic.upf.edu/~ocelma/MusicRecommendationDataset/lastfm-1K.html.

  8. 8.

    http://labrosa.ee.columbia.edu/millionsong/lastfm.

  9. 9.

    http://socialcomputing.asu.edu/datasets/Last.fm.

  10. 10.

    http://www.yelp.com/dataset_challenge.

  11. 11.

    For more detailed information about random walk and random walk with restart, refer to [126].

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Authors and Affiliations

  1. Department of Software, Sangmyung University, 31, Sangmyungdae-gil, Dongnam-gu, Cheonan-si, Chungcheongnam-do, 31066, Korea

    Danielle Lee

  2. School of Computing and Information, University of Pittsburgh, 135 North Bellefield Avenue, Pittsburgh, PA, 15260, USA

    Peter Brusilovsky

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    Peter Brusilovsky

  2. University of Pittsburgh, Pittsburgh, Pennsylvania, USA

    Daqing He

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Lee, D., Brusilovsky, P. (2018). Recommendations Based on Social Links. 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_11

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