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Book Recommendation Beyond the Usual Suspects

Embedding Book Plots Together with Place and Time Information
  • Julian RischEmail author
  • Samuele Garda
  • Ralf Krestel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11279)

Abstract

Content-based recommendation of books and other media is usually based on semantic similarity measures. While metadata can be compared easily, measuring the semantic similarity of narrative literature is challenging. Keyword-based approaches are biased to retrieve books of the same series or do not retrieve any results at all in sparser libraries. We propose to represent plots with dense vectors to foster semantic search for similar plots even if they do not have any words in common. Further, we propose to embed plots, places, and times in the same embedding space. Thereby, we allow arithmetics on these aspects. For example, a book with a similar plot but set in a different, user-specified place can be retrieved. We evaluate our findings on a set of 16,000 book synopses that spans literature from 500 years and 200 genres and compare our approach to a keyword-based baseline.

Keywords

Recommender systems Text mining Document embedding 

References

  1. 1.
    Bamman, D., Smith, N.A.: New alignment methods for discriminative book summarization. arXiv:1305.1319 (2013)
  2. 2.
    Bogers, T., Koolen, M.: Defining and supporting narrative-driven recommendation. In: Proceedings of the Confernece on Recommender Systems (RecSys), pp. 238–242. ACM (2017)Google Scholar
  3. 3.
    Bogers, T., Petras, V.: An in-depth analysis of tags and controlled metadata for book search. In: iConference, vol. 2, pp. 15–30 (2017)Google Scholar
  4. 4.
    Bogers, T., Petras, V.: Supporting book search: a comprehensive comparison of tags vs. controlled vocabulary metadata. Data Inf. Manag. 1, 17–34 (2017)Google Scholar
  5. 5.
    Charalampous, A., Knoth, P.: Classifying document types to enhance search and recommendations in digital libraries. In: Kamps, J., Tsakonas, G., Manolopoulos, Y., Iliadis, L., Karydis, I. (eds.) TPDL 2017. LNCS, vol. 10450, pp. 181–192. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-67008-9_15CrossRefGoogle Scholar
  6. 6.
    Khusro, S., Ullah, I.: Towards a semantic book search engine. In: Proceedings of the International Conference on Open Source Systems & Technologies (ICOSST), pp. 106–113 (2016)Google Scholar
  7. 7.
    Koolen, M., Bogers, T., van den Bosch, A., Kamps, J.: Looking for books in social media: an analysis of complex search requests. In: Hanbury, A., Kazai, G., Rauber, A., Fuhr, N. (eds.) ECIR 2015. LNCS, vol. 9022, pp. 184–196. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-16354-3_19CrossRefGoogle Scholar
  8. 8.
    Latard, B., Weber, J., Forestier, G., Hassenforder, M.: Towards a semantic search engine for scientific articles. In: Kamps, J., Tsakonas, G., Manolopoulos, Y., Iliadis, L., Karydis, I. (eds.) TPDL 2017. LNCS, vol. 10450, pp. 608–611. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-67008-9_54CrossRefGoogle Scholar
  9. 9.
    Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: Proceedings of the International Conference on Machine Learning (ICML), pp. 1188–1196. JMLR (2014)Google Scholar
  10. 10.
    van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)zbMATHGoogle Scholar
  11. 11.
    Mesbah, S., Fragkeskos, K., Lofi, C., Bozzon, A., Houben, G.-J.: Facet embeddings for explorative analytics in digital libraries. In: Kamps, J., Tsakonas, G., Manolopoulos, Y., Iliadis, L., Karydis, I. (eds.) TPDL 2017. LNCS, vol. 10450, pp. 86–99. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-67008-9_8CrossRefGoogle Scholar
  12. 12.
    Mihalcea, R., Corley, C., Strapparava, C., et al.: Corpus-based and knowledge-based measures of text semantic similarity. AAAI 6, 775–780 (2006)Google Scholar
  13. 13.
    Mikolov, T., Yih, W.t., Zweig, G.: Linguistic regularities in continuous space word representations. In: Proceedings of the Conference of the North American Chapter of the ACL (NAACL), pp. 746–751. ACL (2013)Google Scholar
  14. 14.
    Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)Google Scholar
  15. 15.
    Reuter, K.: Assessing aesthetic relevance: children’s book selection in a digital library. J. Assoc. Inf. Sci. Technol. (JAIST) 58(12), 1745–1763 (2007)CrossRefGoogle Scholar
  16. 16.
    Risch, J., Krestel, R.: What should i cite? Cross-collection reference recommendation of patents and papers. In: Kamps, J., Tsakonas, G., Manolopoulos, Y., Iliadis, L., Karydis, I. (eds.) TPDL 2017. LNCS, vol. 10450, pp. 40–46. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-67008-9_4CrossRefGoogle Scholar
  17. 17.
    Socher, R., et al.: Recursive deep models for semantic compositionality over a sentiment TreeBank. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1631–1642. ACL (2013)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Hasso Plattner InstituteUniversity of PotsdamPotsdamGermany

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