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On the Effectiveness of Convolutional Autoencoders on Image-Based Personalized Recommender Systems
dc.contributor.author | Blanco Mallo, Eva | |
dc.contributor.author | Remeseiro, Beatriz | |
dc.contributor.author | Bolón-Canedo, Verónica | |
dc.contributor.author | Alonso-Betanzos, Amparo | |
dc.date.accessioned | 2020-10-28T16:24:56Z | |
dc.date.available | 2020-10-28T16:24:56Z | |
dc.date.issued | 2020-08-19 | |
dc.identifier.citation | Blanco-Mallo, E.; Remeseiro, B.; Bolón-Canedo, V.; Alonso-Betanzos, A. On the Effectiveness of Convolutional Autoencoders on Image-Based Personalized Recommender Systems. Proceedings 2020, 54, 11. https://doi.org/10.3390/proceedings2020054011Blanco-Mallo, E.; Remeseiro, B.; Bolón-Canedo, V.; Alonso-Betanzos, A. On the Effectiveness of Convolutional Autoencoders on Image-Based Personalized Recommender Systems. Proceedings 2020, 54, 11. https://doi.org/10.3390/proceedings2020054011 | es_ES |
dc.identifier.issn | 2504-3900 | |
dc.identifier.uri | http://hdl.handle.net/2183/26576 | |
dc.description.abstract | [Abstract] Over the years, the success of recommender systems has become remarkable. Due to the massive arrival of options that a consumer can have at his/her reach, a collaborative environment was generated, where users from all over the world seek and share their opinions based on all types of products. Specifically, millions of images tagged with users’ tastes are available on the web. Therefore, the application of deep learning techniques to solve these types of tasks has become a key issue, and there is a growing interest in the use of images to solve them, particularly through feature extraction. This work explores the potential of using only images as sources of information for modeling users’ tastes and proposes a method to provide gastronomic recommendations based on them. To achieve this, we focus on the pre-processing and encoding of the images, proposing the use of a pre-trained convolutional autoencoder as feature extractor. We compare our method with the standard approach of using convolutional neural networks and study the effect of applying transfer learning, reflecting how it is better to use only the specific knowledge of the target domain in this case, even if fewer examples are available. | es_ES |
dc.description.sponsorship | This research has been financially supported in part by European Union FEDER funds, by the Spanish Ministerio de Economía y Competitividad (research project PID2019-109238GB), by the Consellería de Industria of the Xunta de Galicia (research project GRC2014/035), and by the Principado de Asturias Regional Government (research project IDI-2018-000176). CITIC as a Research Center of the Galician University System is financed by the Consellería de Educación, Universidades e Formación Profesional (Xunta de Galicia) through the ERDF (80%), Operational Programme ERDF Galicia 2014–2020 and the remaining 20% by the Secretaria Xeral de Universidades (ref. ED431G 2019/01). | es_ES |
dc.description.sponsorship | Xunta de Galicia; GRC2014/035 | es_ES |
dc.description.sponsorship | Gobierno del Principado de Asturias; IDI-2018-000176 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI AG | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-109238GB-C21/ES/SISTEMAS DE RECOMENDACION EXPLICABLES | |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-109238GB-C22/ES/APRENDIZAJE AUTOMATICO ESCALABLE Y EXPLICABLE | |
dc.relation.uri | https://doi.org/10.3390/proceedings2020054011 | es_ES |
dc.rights | Atribución 4.0 Internacional | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Personalized recommendation | es_ES |
dc.subject | Image-based recommendation system | es_ES |
dc.subject | Feature extraction | es_ES |
dc.subject | Convolutional autoencoder | es_ES |
dc.subject | Convolutional neural network | es_ES |
dc.subject | Data augmentation | es_ES |
dc.title | On the Effectiveness of Convolutional Autoencoders on Image-Based Personalized Recommender Systems | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Proceedings | es_ES |
UDC.volume | 54 | es_ES |
UDC.issue | 1 | es_ES |
UDC.startPage | 11 | es_ES |
dc.identifier.doi | 10.3390/proceedings2020054011 | |
UDC.conferenceTitle | 3rd XoveTIC Conference; A Coruña, Spain; 8–9 October 2020 | es_ES |