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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Bamman, D., Smith, N.A.: New alignment methods for discriminative book summarization. arXiv:1305.1319 (2013)
Bogers, T., Koolen, M.: Defining and supporting narrative-driven recommendation. In: Proceedings of the Confernece on Recommender Systems (RecSys), pp. 238–242. ACM (2017)
Bogers, T., Petras, V.: An in-depth analysis of tags and controlled metadata for book search. In: iConference, vol. 2, pp. 15–30 (2017)
Bogers, T., Petras, V.: Supporting book search: a comprehensive comparison of tags vs. controlled vocabulary metadata. Data Inf. Manag. 1, 17–34 (2017)
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_15
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)
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_19
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_54
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)
van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)
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_8
Mihalcea, R., Corley, C., Strapparava, C., et al.: Corpus-based and knowledge-based measures of text semantic similarity. AAAI 6, 775–780 (2006)
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)
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)
Reuter, K.: Assessing aesthetic relevance: children’s book selection in a digital library. J. Assoc. Inf. Sci. Technol. (JAIST) 58(12), 1745–1763 (2007)
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_4
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Risch, J., Garda, S., Krestel, R. (2018). Book Recommendation Beyond the Usual Suspects. In: Dobreva, M., Hinze, A., Žumer, M. (eds) Maturity and Innovation in Digital Libraries. ICADL 2018. Lecture Notes in Computer Science(), vol 11279. Springer, Cham. https://doi.org/10.1007/978-3-030-04257-8_24
Download citation
DOI: https://doi.org/10.1007/978-3-030-04257-8_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04256-1
Online ISBN: 978-3-030-04257-8
eBook Packages: Computer ScienceComputer Science (R0)