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Novel and Diverse Recommendations by Leveraging Linear Models with User and Item Embeddings
dc.contributor.author | Landin, Alfonso | |
dc.contributor.author | Parapar, Javier | |
dc.contributor.author | Barreiro, Alvaro | |
dc.date.accessioned | 2020-05-18T14:16:45Z | |
dc.date.available | 2020-05-18T14:16:45Z | |
dc.date.issued | 2020-04-08 | |
dc.identifier.citation | Landin A., Parapar J., Barreiro Á. (2020) Novel and Diverse Recommendations by Leveraging Linear Models with User and Item Embeddings. In: Jose J. et al. (eds) Advances in Information Retrieval. ECIR 2020. Lecture Notes in Computer Science, vol 12036. Springer, Cham. https://doi.org/10.1007/978-3-030-45442-5_27 | es_ES |
dc.identifier.isbn | 9783030454418 | |
dc.identifier.isbn | 9783030454425 | |
dc.identifier.issn | 0302-9743 | |
dc.identifier.uri | http://hdl.handle.net/2183/25591 | |
dc.description.abstract | [Abstract] Nowadays, item recommendation is an increasing concern for many companies. Users tend to be more reactive than proactive for solving information needs. Recommendation accuracy became the most studied aspect of the quality of the suggestions. However, novel and diverse suggestions also contribute to user satisfaction. Unfortunately, it is common to harm those two aspects when optimizing recommendation accuracy. In this paper, we present EER, a linear model for the top-N recommendation task, which takes advantage of user and item embeddings for improving novelty and diversity without harming accuracy. | es_ES |
dc.description.sponsorship | This work was supported by project RTI2018-093336-B-C22 (MCIU & ERDF), project GPC ED431B 2019/03 (Xunta de Galicia & ERDF) and accreditation ED431G 2019/01 (Xunta de Galicia & ERDF). The first author also acknowledges the support of grant FPU17/03210 (MCIU) | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431B 2019/03 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Springer | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-093336-B-C22/ES/TECNOLOGIAS PARA LA PREDICCION TEMPRANA DE SIGNOS RELACIONADOS CON TRASTORNOS PSICOLOGICOS (SUBPROYECTO UDC) | |
dc.relation | info:eu-repo/grantAgreement/MECD/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/FPU17%2F03210/ES/ | |
dc.relation.uri | https://doi.org/10.1007/978-3-030-45442-5_27 | es_ES |
dc.rights | © Springer Nature Switzerland AG 2020 | |
dc.subject | Collaborative filtering | es_ES |
dc.subject | Novelty | es_ES |
dc.subject | Diversity | es_ES |
dc.subject | User and item embeddings | es_ES |
dc.title | Novel and Diverse Recommendations by Leveraging Linear Models with User and Item Embeddings | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Lecture Notes in Computer Science | es_ES |
UDC.volume | 12036 | es_ES |
UDC.startPage | 215 | es_ES |
UDC.endPage | 222 | es_ES |
dc.identifier.doi | 10.1007/978-3-030-45442-5_27 | |
UDC.conferenceTitle | 42nd European Conference on IR Research, ECIR 2020, Lisbon, Portugal, April 14–17, 2020 | es_ES |