Skip to main content
SpringerLink
Log in

Tinderbook: Fall in Love with Culture

  • Conference paper
  • First Online:
The Semantic Web (ESWC 2019)

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

Included in the following conference series:

  • 2690 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

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)

    Article  Google Scholar 

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

    Article  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)

    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)

    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

    Chapter  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)

    Article  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)

    Article  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

    Chapter  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

    Chapter  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)

    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)

    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)

    Article  MathSciNet  MATH  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

  1. LINKS Foundation, Turin, Italy

    Enrico Palumbo, Alberto Buzio, Andrea Gaiardo & Giuseppe Rizzo

  2. EURECOM, Sophia Antipolis, France

    Enrico Palumbo & Raphael Troncy

  3. Politecnico di Torino, Turin, Italy

    Enrico Palumbo & Elena Baralis

Authors
  1. Enrico Palumbo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alberto Buzio
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Andrea Gaiardo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Giuseppe Rizzo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Raphael Troncy
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Elena Baralis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Enrico Palumbo .

Editor information

Editors and Affiliations

  1. Wright State University, Dayton, OH, USA

    Pascal Hitzler

  2. KMi, The Open University, Milton Keynes, UK

    Miriam Fernández

  3. University of California, Santa Barbara, CA, USA

    Krzysztof Janowicz

  4. Maastricht University, Maastricht, The Netherlands

    Amrapali Zaveri

  5. Heriot-Watt University, Edinburgh, UK

    Alasdair J.G. Gray

  6. IBM Research, Dublin, Ireland

    Vanessa Lopez

  7. The Australian National University, Canberra, ACT, Australia

    Armin Haller

  8. Jönköping University, Jönköping, Sweden

    Karl Hammar

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. 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: Hitzler, P., 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)

Publish with us

Policies and ethics

Search

Navigation