Personalized Book Recommendation Based on a Deep Learning Model and Metadata

  • Yiu-Kai NgEmail author
  • Urim Jung
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11881)


Reading books is one of the widely-adopted methods to obtain knowledge. Through reading books, one can obtain life-long knowledge and maintain them. Additionally, if multiple sources of information can be obtained from various books, then obtaining relevant books is desirable. This can be done by book recommendation. There are, however, a number of challenges in designing a book recommender system. One of the challenges is to suggest relevant books to users without accessing their actual content. Unlike websites or blogs, where the crawler can simply scrape the content and index the websites for web search, book contents cannot be accessed easily due to copyright laws. Because of this problem, we have considered using data such as book records, which contains various metadata of a book, including book description and headings. In this paper, we propose an elegant and simple solution to the book recommendation problem using a deep learning model and various metadata that can infer the content and the quality of books without utilizing the actual content. Metadata, which include Library Congress Subject Heading (LCSH), book description, user ratings and reviews, which are widely available on the Internet. Using these metadata are relatively simple compared to approaches adopted by existing book recommender systems, yet they provide essential and useful information of books.


Book recommendation Deep learning Metadata 


  1. 1.
    Aslam, J., Montague, M.: Models for metasearch. In: ACM SIGIR, pp. 267–276 (1997)Google Scholar
  2. 2.
    Ding, Z.: The development of ontology information system based on Bayesian network and learning. In: Jin, D., Lin, S. (eds.) Advances in Multimedia, Software Engineering and Computing. Advances in Intelligent and Soft Computing, vol. 129, pp. 401–406. Springer, Heidelberg (2012). Scholar
  3. 3.
    Elmer, E.: Rasmuson Library. University of Alaska Fairbanks, Library of Congress Subject Headings (2014).
  4. 4.
    Givon, S., Lavrenko, V.: Predicting social-tags for cold start book recommendations. In: ACM RecSys. pp. 333–336 (2009)Google Scholar
  5. 5.
    Kleibergen, F., Paap, R.: Generalized reduced rank tests using the singular value decomposition. Econometrics 133(1), 97–126 (2006)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Linden, G., Smith, B., York, J.: recommendations: item-to-item collaborative filtering. Internet Comput. 7(1), 76–80 (2003)CrossRefGoogle Scholar
  7. 7.
    Mooney, R., Roy, L.: Content-based book recommending using learning for text categorization. In: ACM DL 2000, pp. 195–204 (2000)Google Scholar
  8. 8.
    Owusu-Acheaw, M., Larson, A.: Reading habits among students and its effect on academic performance: a study of students of Koforidua Polytechnic. LPP 1130 (2014)Google Scholar
  9. 9.
    Qumsiyeh, R., Ng, Y.: Predicting the ratings of multimedia items for making personalized recommendations. In: SIGIR, pp. 475–484 (2012)Google Scholar
  10. 10.
    Ricci, F., Rokach, L., Shapira, B., Kantor, P.: Recommender System. Handbook. Springer, Cham (2011). Scholar
  11. 11.
    Shi, Y., Larson, M., Hanjalic, A.: Mining mood-specific movie similarity with matrix factorization for context-aware recommendation. In: Workshop on CARS, pp. 34–40 (2010)Google Scholar
  12. 12.
    Siersdorfer, S., Chelaru, S., Nejdl, W., Pedro, J.: How useful are your comments: analyzing and predicting YouTube comments and comment ratings. In: WWW, pp. 891–990 (2010)Google Scholar
  13. 13.
    Singh, A., Gordon, G.: Relational learning via collective matrix factorization. In: ACM SIGKDD, pp. 650–658 (2008)Google Scholar
  14. 14.
    Sohail, S., Siddiqui, J., Ali, R.: Book recommendation system using opinion mining technique. In: IEEE ICACCI, pp. 1609–1614 (2013)Google Scholar
  15. 15.
    Yang, C., Wei, B., Wu, J., Zhang, Y., Zhang, L.: CARES: a ranking-oriented CADAL recommender system. In: JCDL, pp. 203–212 (2009)Google Scholar
  16. 16.
    Yu, K., Zhu, S., Lafferty, J., Gong, Y.: Fast nonparametric matrix factorization for large-scale collaborative filtering. In: ACM SIGIR, pp. 211–218 (2009)Google Scholar
  17. 17.
    Zhu, Z., Wang, J.: Book recommendation service by improved association rule mining algorithm. In: ICMLC, pp. 3864–3869 (2007)Google Scholar
  18. 18.
    Ziegler, C., McNee, S., Konstan, J., Lausen, G.: Improving recommendation lists through topic diversification. In: WWW, pp. 22–32 (2005)Google Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Computer Science DepartmentBrigham Young UniversityProvoUSA

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