Active learning and user segmentation for the cold-start problem in recommendation systems

Par Conseil national de recherches du Canada

Téléchargement	Voir la version finale : Active learning and user segmentation for the cold-start problem in recommendation systems (PDF, 1.0 Mio)
DOI	Trouver le DOI : https://doi.org/10.5220/0008162901130123
Auteur	Rechercher : Alabdulrahman, Rabaa; Rechercher : Viktor, Herna; Rechercher : Paquet, Eric¹
Affiliation	Conseil national de recherches du Canada. Technologies numériques
Format	Texte, Article
Conférence	11th International Joint Conference on Knowledge Discovery and Information Retrieval, Sept. 17-19, 2019, Vienna, Austria
Sujet	recommendation systems; collaborative filtering; cold-start; active learning
Résumé	Recommendation systems, which are employed to mitigate the information overload faced by e-commerce users, have succeeded in aiding customers during their online shopping experience. However, to be able to make accurate recommendations, these systems require information about the items for sale and information about users’ individual preferences. Making recommendations to new customers, with no prior data in the system, is therefore challenging. This scenario, called the “cold-start problem,” hinders the accuracy of recommendations made to a new user. In this paper, we introduce the popular users personalized predictions (PUPP) framework to address cold-starts. In this framework, soft clustering and active learning is used to accurately recommend items to new users. Experimental evaluation shows that the PUPP framework results in high performance and accurate predictions. Further, focusing on frequent, or so-called “popular,” users during our active-learning stage clearly benefits the learning process.
Date de publication	2019-09-17
Maison d’édition	SCITEPRESS - Science and Technology Publications
Licence	Creative Commons Attribution - Pas d'utilisation commerciale - Pas de modification 4.0 International (CC BY-NC-ND 4.0) https://creativecommons.org/licenses/by-nc-nd/4.0/deed.fr
Dans	Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management : 113–123.
Langue	anglais
Publications évaluées par des pairs	Oui
Exporter la notice	Exporter en format RIS
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Identificateur de l’enregistrement	e7b1dc71-99dd-4ed1-bf54-d788d9e4c918
Enregistrement créé	2020-11-17
Enregistrement modifié	2020-12-14

Date de modification :: 2024-06-30