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.
TinderBook: Fall in love with culture
ESWC 2019, 16th European Semantic Web Conference, 2-6 June 2019, Portoroz, Slovenia / Also published in LNCS, Vol.11503
© Springer. Personal use of this material is permitted. The definitive version of this paper was published in ESWC 2019, 16th European Semantic Web Conference, 2-6 June 2019, Portoroz, Slovenia / Also published in LNCS, Vol.11503 and is available at : https://doi.org/10.1007/978-3-030-21348-0_38
PERMALINK : https://www.eurecom.fr/publication/5849