Skip to main content

Tinderbook: Fall in Love with Culture

  • 1974 Accesses

Part of the Lecture Notes in Computer Science book series (LNISA,volume 11503)

Abstract

More than 2 millions of new books are published every year and choosing a good book among the huge amount of available options can be a challenging endeavor. Recommender systems help in choosing books by providing personalized suggestions based on the user reading history. However, most book recommender systems are based on collaborative filtering, involving a long onboarding process that requires to rate many books before providing good recommendations. Tinderbook provides book recommendations, given a single book that the user likes, through a card-based playful user interface that does not require an account creation. Tinderbook is strongly rooted in semantic technologies, using the DBpedia knowledge graph to enrich book descriptions and extending a hybrid state-of-the-art knowledge graph embeddings algorithm to derive an item relatedness measure for cold start recommendations. Tinderbook is publicly available (http://www.tinderbook.it) and has already generated interest in the public, involving passionate readers, students, librarians, and researchers. The online evaluation shows that Tinderbook achieves almost 50% of precision of the recommendations.

Keywords

  • Recommender systems
  • Books
  • Knowledge graphs
  • DBpedia
  • Embeddings

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-21348-0_38
  • Chapter length: 16 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   84.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-21348-0
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   109.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.

Notes

  1. 1.

    https://www.telegraph.co.uk/technology/google/7930273/Google-counts-total-number-of-books-in-the-world.html.

  2. 2.

    https://en.wikipedia.org/wiki/Books_published_per_country_per_year.

  3. 3.

    https://www.irisreading.com/how-many-books-does-the-average-person-read/.

  4. 4.

    https://www.nytimes.com/2016/03/15/business/media/moneyball-for-book-publishers-for-a-detailed-look-at-how-we-read.html.

  5. 5.

    https://www.librarything.com.

  6. 6.

    https://wiki.dbpedia.org/services-resources/ontology.

  7. 7.

    https://www.nngroup.com/articles/cards-component/.

  8. 8.

    https://github.com/sisinflab/LODrecsys-datasets/tree/master/LibraryThing.

  9. 9.

    http://dbpedia.org/page/Jurassic_Park_(novel).

References

  1. Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)

    CrossRef  Google Scholar 

  2. Alharthi, H., Inkpen, D., Szpakowicz, S.: A survey of book recommender systems. J. Intell. Inf. Syst. 51(1), 139–160 (2018)

    CrossRef  Google Scholar 

  3. Babich, N.: Designing card-based user interfaces (2016)

    Google Scholar 

  4. Bizer, C., Heath, T., Berners-Lee, T.: Linked data-the story so far. In: Semantic Services, Interoperability and Web Applications: Emerging Concepts, pp. 205–227 (2009)

    CrossRef  Google Scholar 

  5. Cai, T.: The tinder effect: swipe to kiss (keep it simple, stupid!) (2018)

    Google Scholar 

  6. Cousins, C.: The complete guide to an effective card-style interface design (2015)

    Google Scholar 

  7. David, G., Cambre, C.: Screened intimacies: tinder and the swipe logic. Soc. Media+ Soc. 2(2) (2016)

    CrossRef  Google Scholar 

  8. Di Noia, T.: Recommender systems meet linked open data. In: Bozzon, A., Cudre-Maroux, P., Pautasso, C. (eds.) ICWE 2016. LNCS, vol. 9671, pp. 620–623. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-38791-8_61

    CrossRef  Google Scholar 

  9. Di Noia, T., Ostuni, V.C., Tomeo, P., Di Sciascio, E.: SPRank: semantic path-based ranking for top-n recommendations using linked open data. ACM Trans. Intell. Syst. Technol. (TIST) 8(1), 9 (2016)

    Google Scholar 

  10. Figueroa, C., Vagliano, I., Rocha, O.R., Morisio, M.: A systematic literature review of linked data-based recommender systems. Concurrency Comput. Pract. Experience 27(17), 4659–4684 (2015)

    CrossRef  Google Scholar 

  11. Ge, M., Delgado-Battenfeld, C., Jannach, D.: Beyond accuracy: evaluating recommender systems by coverage and serendipity. In: 4th International Conference on Recommender Systems (RecSys), pp. 257–260 (2010)

    Google Scholar 

  12. de Gemmis, M., Lops, P., Semeraro, G., Musto, C.: An investigation on the serendipity problem in recommender systems. Inf. Process. Manage. 51(5), 695–717 (2015)

    CrossRef  Google Scholar 

  13. Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: 22nd International Conference on Knowledge Discovery and Data Mining (SIGKDD), pp. 855–864 (2016)

    Google Scholar 

  14. Lops, P., de Gemmis, M., Semeraro, G.: Content-based recommender systems: state of the art and trends. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 73–105. Springer, Boston (2011). https://doi.org/10.1007/978-0-387-85820-3_3

    CrossRef  Google Scholar 

  15. Palumbo, E., Rizzo, G., Troncy, R.: Entity2rec: learning user-item relatedness from knowledge graphs for top-n item recommendation. In: 11th International Conference on Recommender Systems (RecSys), pp. 32–36 (2017)

    Google Scholar 

  16. Palumbo, E., Rizzo, G., Troncy, R., Baralis, E., Osella, M., Ferro, E.: Knowledge graph embeddings with node2vec for item recommendation. In: European Semantic Web Conference (ESWC), Demo Track, pp. 117–120 (2018)

    Google Scholar 

  17. Palumbo, E., Rizzo, G., Troncy, R., Baralis, E., Osella, M., Ferro, E.: Translational models for item recommendation. In: Gangemi, A., et al. (eds.) ESWC 2018. LNCS, vol. 11155, pp. 478–490. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98192-5_61

    CrossRef  Google Scholar 

  18. Ristoski, P., Rosati, J., Di Noia, T., De Leone, R., Paulheim, H.: RDF2Vec: RDF graph embeddings and their applications. Semant. Web J. 10(4) (2019)

    CrossRef  Google Scholar 

  19. Rosati, J., Ristoski, P., Di Noia, T., de Leone, R., Paulheim, H.: RDF graph embeddings for content-based recommender systems. In: CEUR Workshop Proceedings, vol. 1673, pp. 23–30 (2016)

    Google Scholar 

  20. Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: 10th International World Wide Web Conference, pp. 285–295 (2001)

    Google Scholar 

  21. Shi, C., Hu, B., Zhao, X., Yu, P.: Heterogeneous information network embedding for recommendation. IEEE Trans. Knowl. Data Eng. 357–370 (2018)

    CrossRef  Google Scholar 

  22. Steck, H.: Evaluation of recommendations: rating-prediction and ranking. In: 7th ACM Conference on Recommender Systems, pp. 213–220 (2013)

    Google Scholar 

  23. Vargas, S., Castells, P.: Rank and relevance in novelty and diversity metrics for recommender systems. In: 5th ACM Conference on Recommender Systems, pp. 109–116 (2011)

    Google Scholar 

  24. Welch, B.L.: The generalization of student’s problem when several different population variances are involved. Biometrika 34(1/2), 28–35 (1947)

    MathSciNet  CrossRef  Google Scholar 

  25. Zhang, F., Yuan, N.J., Lian, D., Xie, X., Ma, W.Y.: Collaborative knowledge base embedding for recommender systems. In: 22nd International Conference on Knowledge Discovery and Data Mining (SIGKDD), pp. 353–362 (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Enrico Palumbo .

Editor information

Editors and Affiliations

Rights and permissions

Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Reprints and Permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Palumbo, E., Buzio, A., Gaiardo, A., Rizzo, G., Troncy, R., Baralis, E. (2019). Tinderbook: Fall in Love with Culture. In: , et al. The Semantic Web. ESWC 2019. Lecture Notes in Computer Science(), vol 11503. Springer, Cham. https://doi.org/10.1007/978-3-030-21348-0_38

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-21348-0_38

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-21347-3

  • Online ISBN: 978-3-030-21348-0

  • eBook Packages: Computer ScienceComputer Science (R0)