Author, as appears in the article.: Orama, Jonathan Ayebakuro; Borras, Joan; Moreno, Antonio;
Department: Economia
URV's Author/s: Borràs Nogués, Joan / Moreno Ribas, Antonio / Orama, Ayebakuro Jonathan
Keywords: User profiling Systems Social media Recommender systems Cluster analysis Association rule mining
Abstract: Tourists who visit a city for the first time may find it difficult to decide on places to visit, as the amount of information in the Web about cultural and leisure activities may be large. Recommender systems address this problem by suggesting the points of interest that fit better with the user's preferences. This paper presents a novel recommender system that leverages tweets to build user profiles, taking into account not only their personal preferences but also their travel habits. Association rules, which are mined from the previous visits of users documented on Twitter, are used to make the final recommendations of places to visit. The system has been applied to data of the city of Barcelona, and the results show that the use of the social media-based clustering procedure increases its performance according to several relevant metrics.
Thematic Areas: Química Process chemistry and technology Physics, applied Materials science, multidisciplinary Materials science (miscellaneous) Materials science (all) Materiais Instrumentation General materials science General engineering Fluid flow and transfer processes Engineering, multidisciplinary Engineering (miscellaneous) Engineering (all) Engenharias ii Engenharias i Computer science applications Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciências agrárias i Ciência de alimentos Chemistry, multidisciplinary Biodiversidade Astronomia / física
licence for use: https://creativecommons.org/licenses/by/3.0/es/
Author's mail: joan.borrasn@urv.cat antonio.moreno@urv.cat ayebakurojonathan.orama@estudiants.urv.cat
Author identifier: 0000-0002-3894-4010 0000-0003-3945-2314 0000-0002-2622-3224
Record's date: 2023-03-18
Papper version: info:eu-repo/semantics/publishedVersion
Link to the original source: https://www.mdpi.com/2076-3417/11/14/6512
Licence document URL: http://repositori.urv.cat/ca/proteccio-de-dades/
Papper original source: Applied Sciences-Basel. 11 (14):
APA: Orama, Jonathan Ayebakuro; Borras, Joan; Moreno, Antonio; (2021). Combining Cluster-Based Profiling Based on Social Media Features and Association Rule Mining for Personalised Recommendations of Touristic Activities. Applied Sciences-Basel, 11(14), -. DOI: 10.3390/app11146512
Article's DOI: 10.3390/app11146512
Entity: Universitat Rovira i Virgili
Journal publication year: 2021
Publication Type: Journal Publications
Repositori URV