Aspect-based active learning for user preference elicitation in recommender systems
Entity
UAM. Departamento de Ingeniería InformáticaPublisher
CEUR Workshop ProceedingsDate
2020-07Citation
Proceedings of the 1st Joint Conference of the Information Retrieval Communities (CIRCLE 2020). CEUR, 2022.ISSN
1613-0073 (print)Funded by
This work was supported by the Spanish Ministry of Science and Innovation (PID2019-108965GB-I00)Project
Gobierno de España. PID2019-108965GB-I00Editor's Version
https://ceur-ws.org/Vol-2621/Subjects
User preference elicitation; Active learning; User reviews; Opinion mining; Recommender systems; InformáticaRights
© The author(s)Abstract
Recommender systems require interactions from users to infer personal preferences about new items. Active learning techniques aim to identify those items that allow eliciting a target user’s preferences more efficiently. Most of the existing techniques base their decisions on properties of the items themselves, for example according to their popularity or in terms of their influence on reducing information variance or entropy within the system. Differently to previous work, in this paper we explore a novel active learning approach focused on opinions about item aspects extracted from user reviews. We thus incorporate textual information so as to decide which items should be considered next in the user preference elicitation process. Experiments on a real-world dataset provide
positive results with respect to competitive state of the art methods
Files in this item
Google Scholar:Hernández Rubio, María
-
Bellogin Kouki, Alejandro
-
Cantador Gutiérrez, Iván
This item appears in the following Collection(s)
Related items
Showing items related by title, author, creator and subject.