Tinderbook: Fall in Love with Culture

  • Enrico PalumboEmail author
  • Alberto Buzio
  • Andrea Gaiardo
  • Giuseppe Rizzo
  • Raphael Troncy
  • Elena Baralis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11503)


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 ( 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.


Recommender systems Books Knowledge graphs DBpedia Embeddings 


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Authors and Affiliations

  1. 1.LINKS FoundationTurinItaly
  2. 2.EURECOMSophia AntipolisFrance
  3. 3.Politecnico di TorinoTurinItaly

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