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Tinderbook: Fall in Love with Culture

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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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  • DOI: 10.1007/978-3-030-21348-0_38
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Correspondence to Enrico Palumbo .

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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.

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