@book{
 title = {Personalized Case-Based Explanation of Matrix Factorization Recommendations},
 type = {book},
 year = {2019},
 source = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
 keywords = {Case-based explanation,Matrix factorization,Personalised explanation},
 volume = {11680 LNAI},
 id = {03812034-5bf2-3f43-84d9-da6d355e8bd1},
 created = {2019-10-11T23:59:00.000Z},
 file_attached = {false},
 profile_id = {9b41408a-6b73-3771-9777-d165eecfa568},
 last_modified = {2021-01-13T11:39:46.179Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {true},
 abstract = {© 2019, Springer Nature Switzerland AG. Matrix factorization is an advanced recommendation strategy based on characterizing both items and users on a vector of latent factors inferred from rating patterns. These vectors represent, somehow, a characterization of the user preferences in a lower dimensionality space. Although matrix factorization is more accurate that other recommendation strategies, the main problem associated with this approach is that the discovered factors are opaque and difficult to explain to the final user. In this paper we propose a personalized case-based explanation strategy that uses the latent factors to find similar explanatory cases already rated by the user.},
 bibtype = {book},
 author = {Jorro-Aragoneses, J. and Caro-Martinez, M. and Recio-Garcia, J.A. and Diaz-Agudo, B. and Jimenez-Diaz, G.},
 doi = {10.1007/978-3-030-29249-2_10}
}

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