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A survey of book recommender systems

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Abstract

The act of reading has benefits for individuals and societies, yet studies show that reading declines, especially among the young. Recommender systems can help stop such decline. We present a survey of recommender systems in the domain of books. We have categorized the systems into six classes, and highlighted the main trends, issues, evaluation approaches and datasets. Other research areas, such as psychology, are consulted to understand users’ books choices and reading models.

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Notes

  1. http://tinyurl.com/jgunwfx

  2. http://tinyurl.com/24vypnk

  3. http://tinyurl.com/kf3k7m3

  4. http://tinyurl.com/j2jsoml

  5. In a closed dataset, the rating matrix has no missing values: every user rates every book.

  6. http://tinyurl.com/je3662l

  7. http://tinyurl.com/hgc4tn2

  8. https://www.mturk.com/mturk/welcome

  9. http://wiki.dbpedia.org/

  10. http://tinyurl.com/jptm5jg

  11. http://www.macle.nl/tud/LT/

  12. http://sisinflab.poliba.it/semanticweb/lod/recsys/datasets/

  13. http://tinyurl.com/zgrba6f

  14. http://www.goodreads.com/about/us

  15. An Amazon service selling audiobooks: http://www.audible.com

  16. http://tinyurl.com/ybg9jshb

  17. http://www.fatml.org

  18. http://home.earthlink.net/dwaha/research/meetings/ijcai17-xai/

  19. http://www.interpretable-ml.org/icann2016workshop/

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Acknowledgements

The first author receives support from the Saudi Electronic University. This work was also partially supported by the Natural Sciences and Engineering Research Council of Canada.

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  1. University of Ottawa, Ottawa, Ontario, Canada

    Haifa Alharthi, Diana Inkpen & Stan Szpakowicz

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  1. Haifa Alharthi
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Correspondence to Haifa Alharthi.

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Alharthi, H., Inkpen, D. & Szpakowicz, S. A survey of book recommender systems. J Intell Inf Syst 51, 139–160 (2018). https://doi.org/10.1007/s10844-017-0489-9

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