![]() ![]() In: 22nd International Conference on Knowledge Discovery and Data Mining (SIGKDD), pp.Self-publishing is a great way to get your book into the world. Zhang, F., Yuan, N.J., Lian, D., Xie, X., Ma, W.Y.: Collaborative knowledge base embedding for recommender systems. Welch, B.L.: The generalization of student’s problem when several different population variances are involved. In: 5th ACM Conference on Recommender Systems, pp. Vargas, S., Castells, P.: Rank and relevance in novelty and diversity metrics for recommender systems. In: 7th ACM Conference on Recommender Systems, pp. Steck, H.: Evaluation of recommendations: rating-prediction and ranking. Shi, C., Hu, B., Zhao, X., Yu, P.: Heterogeneous information network embedding for recommendation. In: 10th International World Wide Web Conference, pp. Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. Rosati, J., Ristoski, P., Di Noia, T., de Leone, R., Paulheim, H.: RDF graph embeddings for content-based recommender systems. Ristoski, P., Rosati, J., Di Noia, T., De Leone, R., Paulheim, H.: RDF2Vec: RDF graph embeddings and their applications. Palumbo, E., Rizzo, G., Troncy, R., Baralis, E., Osella, M., Ferro, E.: Translational models for item recommendation. In: European Semantic Web Conference (ESWC), Demo Track, pp. Palumbo, E., Rizzo, G., Troncy, R., Baralis, E., Osella, M., Ferro, E.: Knowledge graph embeddings with node2vec for item recommendation. In: 11th International Conference on Recommender Systems (RecSys), pp. Palumbo, E., Rizzo, G., Troncy, R.: Entity2rec: learning user-item relatedness from knowledge graphs for top-n item recommendation. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. Lops, P., de Gemmis, M., Semeraro, G.: Content-based recommender systems: state of the art and trends. In: 22nd International Conference on Knowledge Discovery and Data Mining (SIGKDD), pp. Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. 257–260 (2010)ĭe Gemmis, M., Lops, P., Semeraro, G., Musto, C.: An investigation on the serendipity problem in recommender systems. In: 4th International Conference on Recommender Systems (RecSys), pp. Ge, M., Delgado-Battenfeld, C., Jannach, D.: Beyond accuracy: evaluating recommender systems by coverage and serendipity. (TIST) 8(1), 9 (2016)įigueroa, C., Vagliano, I., Rocha, O.R., Morisio, M.: A systematic literature review of linked data-based recommender systems. ![]() ![]() ĭi Noia, T., Ostuni, V.C., Tomeo, P., Di Sciascio, E.: SPRank: semantic path-based ranking for top-n recommendations using linked open data. In: Bozzon, A., Cudre-Maroux, P., Pautasso, C. 2(2) (2016)ĭi Noia, T.: Recommender systems meet linked open data. 205–227 (2009)Ĭai, T.: The tinder effect: swipe to kiss (keep it simple, stupid!) (2018)Ĭousins, C.: The complete guide to an effective card-style interface design (2015)ĭavid, G., Cambre, C.: Screened intimacies: tinder and the swipe logic. In: Semantic Services, Interoperability and Web Applications: Emerging Concepts, pp. 51(1), 139–160 (2018)īabich, N.: Designing card-based user interfaces (2016)īizer, C., Heath, T., Berners-Lee, T.: Linked data-the story so far. ![]() 17(6), 734–749 (2005)Īlharthi, H., Inkpen, D., Szpakowicz, S.: A survey of book recommender systems. KeywordsĪdomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. The online evaluation shows that Tinderbook achieves almost 50% of precision of the recommendations. Tinderbook is publicly available ( ) and has already generated interest in the public, involving passionate readers, students, librarians, and researchers. 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 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. 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. Recommender systems help in choosing books by providing personalized suggestions based on the user reading history. 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. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |