@article{
 title = {RecoLibry-core: A component-based framework for building recommender systems},
 type = {article},
 year = {2019},
 pages = {In Press},
 websites = {https://www.sciencedirect.com/science/article/pii/S0950705119303296},
 month = {7},
 publisher = {Elsevier},
 day = {25},
 id = {3b19fa96-50fd-3ffc-99ab-feccf08e9f73},
 created = {2019-08-20T10:49:41.602Z},
 accessed = {2019-08-20},
 file_attached = {false},
 profile_id = {9b41408a-6b73-3771-9777-d165eecfa568},
 group_id = {417c88cb-beff-360c-a95a-7fe124855ec9},
 last_modified = {2019-08-20T10:49:41.602Z},
 read = {false},
 starred = {true},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Recommendation systems are a key part of almost every modern consumer website. These systems include techniques to filter, explore and rank a huge amount of information based on users’ preferences or similar items. Designing and implementing a recommender system from scratch require skills of programming and recommending technologies. In this paper we describe RecoLibry-core, a framework to develop recommender systems based on the reuse of components provided by third-party frameworks.},
 bibtype = {article},
 author = {Jorro-Aragoneses, Jose L. and Recio-García, Juan A. and Díaz-Agudo, Belén and Jimenez-Diaz, Guillermo},
 doi = {10.1016/J.KNOSYS.2019.07.025},
 journal = {Knowledge-Based Systems}
}

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