A genetic-based pairwise trip planner recommender system

Autor:	Nunung Nurul Qomariyah, Dimitar Kazakov
Jazyk:	angličtina
Rok vydání:	2021
Předmět:	Recommender system Pairwise preferences Genetic algorithm Preference learning Computer engineering. Computer hardware TK7885-7895 Information technology T58.5-58.64 Electronic computers. Computer science QA75.5-76.95
Zdroj:	Journal of Big Data, Vol 8, Iss 1, Pp 1-23 (2021)
Druh dokumentu:	article
ISSN:	2196-1115 45029822
DOI:	10.1186/s40537-021-00470-6
Popis:	Abstract The massive growth of internet users nowadays can be a big opportunity for the businesses to promote their services. This opportunity is not only for e-commerce, but also for other e-services, such as e-tourism. In this paper, we propose an approach of personalized recommender system with pairwise preference elicitation for the e-tourism domain area. We used a combination of Genetic Agorithm with pairwise user preference elicitation approach. The advantages of pairwise preference elicitation method, as opposed to the pointwise method, have been shown in many studies, including to reduce incosistency and confusion of a rating number. We also performed a user evaluation study by inviting 24 participants to examine the proposed system and publish the POIs dataset which contains 201 attractions used in this study.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/5266ba061c3c4502982293f824b391d7 Zobrazit plný text záznamu Full text from SpringerLink View record in DOAJ