dc.contributor.author | Khadka, Anita | es_ES |
dc.contributor.author | Cantador Gutiérrez, Iván | es_ES |
dc.contributor.author | Fernandez, Miriam | es_ES |
dc.contributor.editor | Expósito, Ernesto | es_ES |
dc.contributor.other | UAM. Departamento de Ingeniería Informática | es_ES |
dc.date.accessioned | 2022-07-08T09:18:34Z | en_US |
dc.date.available | 2022-07-08T09:18:34Z | en_US |
dc.date.issued | 2020-11-01 | en_US |
dc.identifier.citation | 2020 IEEE 29th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE). IEEE, 2020. 239-244 | en_US |
dc.identifier.isbn | 9781728169750 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10486/702977 | en_US |
dc.description | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works | en_US |
dc.description.abstract | With the continuous growth of scientific literature, discovering relevant academic papers for a researcher has become a challenging task, especially when looking for the latest, most recent papers. In this case, traditional collaborative filtering systems are ineffective, since they are unable to recommend items not previously seen, rated or cited. This is known as the item cold-start problem. In this paper, we explore the potential of exploiting citation knowledge to provide a given user with relevant suggestions about recent scientific publications. A novel hybrid recommendation method that encapsulates such citation knowledge is proposed. Experimental results show improvements over baseline methods, evidencing benefits of using citation knowledge to recommend recently published papers in a personalised way. Moreover, as a result of our work, we also provide a unique dataset that, differently to previous corpora, contains detailed paper citation information | en_US |
dc.format.extent | 7 pag. | en_US |
dc.format.mimetype | application/pdf | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.relation.ispartof | Proceedings of the Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises, WETICE | en_US |
dc.rights | © Institute of Electrical and Electronics Engineers | en_US |
dc.subject.other | academic citations | en_US |
dc.subject.other | dataset | en_US |
dc.subject.other | recommender systems | en_US |
dc.subject.other | Scientific publications | en_US |
dc.title | Capturing and Exploiting Citation Knowledge for Recommending Recently Published Papers | en_US |
dc.type | bookPart | en_US |
dc.type | conferenceObject | en_US |
dc.subject.eciencia | Informática | es_ES |
dc.relation.publisherversion | http://doi.org/10.1109/WETICE49692.2020.00054 | es_ES |
dc.identifier.doi | 10.1109/WETICE49692.2020.00054 | es_ES |
dc.identifier.publicationfirstpage | 239 | es_ES |
dc.identifier.publicationlastpage | 244 | es_ES |
dc.type.version | info:eu-repo/semantics/acceptedVersion | en_US |
dc.rights.accessRights | openAccess | en_US |
dc.facultadUAM | Escuela Politécnica Superior | es_ES |