dc.contributor.author | Segura Tinoco, German Andrés | es_ES |
dc.contributor.other | UAM. Departamento de Ingeniería Informática | es_ES |
dc.date.accessioned | 2022-11-15T18:33:26Z | en_US |
dc.date.available | 2022-11-15T18:33:26Z | en_US |
dc.date.issued | 2021-09-13 | en_US |
dc.identifier.citation | Fifteenth ACM Conference on Recommender Systems, RecSys, ACM, 2021. 845-850 | en_US |
dc.identifier.isbn | 978-1-4503-8458-2 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10486/705253 | en_US |
dc.description.abstract | In the recommender systems literature, it has been shown that, in addition to improving system effectiveness, explaining recommendations may increase user satisfaction, trust, persuasion and loyalty. In general, explanations focus on the filtering algorithms or the users and items involved in the generation of recommendations. However, on certain domains that are rich on user-generated textual content, it would be valuable to provide justifications of recommendations according to arguments that are explicit, underlying or related with the data used by the systems, e.g., the reasons for customers' opinions in reviews of e-commerce sites, and the requests and claims in citizens' proposals and debates of e-participation platforms. In this context, there is a need and challenging task to automatically extract and exploit the arguments given for and against evaluated items. We thus advocate to focus not only on user preferences and item features, but also on associated arguments. In other words, we propose to not only consider what is said about items, but also why it is said. Hence, arguments would not only be part of the recommendation explanations, but could also be used by the recommendation algorithms themselves. To this end, in this thesis, we propose to use argument mining techniques and tools that allow retrieving and relating argumentative information from textual content, and investigate recommendation methods that exploit that information before, during and after their filtering processes | en_US |
dc.description.sponsorship | The author thanks his supervisor Iván Cantador for his valuable support and guidance in defining this thesis project. The work
is supported by the Spanish Ministry of Science and Innovation (PID2019-108965GB-I00) | en_US |
dc.format.extent | 7 pag. | en_US |
dc.format.mimetype | application/pdf | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Association for Computing Machinery | en_US |
dc.relation.ispartof | ACM Conference on Recommender Systems, RECSYS 2021 | en_US |
dc.rights | © The author(s) | en_US |
dc.subject.other | Argument mining | en_US |
dc.subject.other | Natural language processing | en_US |
dc.subject.other | Recommendation explanation | en_US |
dc.subject.other | Recommender systems | en_US |
dc.title | Argument-based generation and explanation of recommendations | en_US |
dc.type | conferenceObject | en_US |
dc.subject.eciencia | Informática | es_ES |
dc.relation.publisherversion | https://doi.org/10.1145/3460231.3473894 | es_ES |
dc.identifier.doi | 10.1145/3460231.3473894 | es_ES |
dc.identifier.publicationfirstpage | 845 | es_ES |
dc.identifier.publicationlastpage | 850 | es_ES |
dc.relation.eventdate | September 27-October 1 | en_US |
dc.relation.eventnumber | 15 | es_ES |
dc.relation.eventplace | Amsterdam (Netherlands) | en_US |
dc.relation.eventtitle | ACM Conference on Recommender Systems, RECSYS 2021 | en_US |
dc.relation.projectID | Gobierno de España. PID2019-108965GB-I00 | 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 |