Mañana, JUEVES, 24 DE ABRIL, el sistema se apagará debido a tareas habituales de mantenimiento a partir de las 9 de la mañana. Lamentamos las molestias.
Argument-based generation and explanation of recommendations
Author
Segura Tinoco, German AndrésEntity
UAM. Departamento de Ingeniería InformáticaPublisher
Association for Computing MachineryDate
2021-09-13Citation
10.1145/3460231.3473894
Fifteenth ACM Conference on Recommender Systems, RecSys, ACM, 2021. 845-850
ISBN
978-1-4503-8458-2DOI
10.1145/3460231.3473894Funded by
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)Project
Gobierno de España. PID2019-108965GB-I00Editor's Version
https://doi.org/10.1145/3460231.3473894Subjects
Argument mining; Natural language processing; Recommendation explanation; Recommender systems; InformáticaRights
© The author(s)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
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Google Scholar:Segura Tinoco, German Andrés
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