Ecole d'ingénieur et centre de recherche en Sciences du numérique

DrIveSCOVER: A tourism recommender system based on external driving factors

Klotz, Benjamin; Lisena, Pasquale; Troncy, Raphaël; Wilms, Daniel; Bonnet, Christian

ISWC 2017, 16th International Semantic Web Conference, Poster Track, October 21-25, 2017, Vienna, Austria

In this paper, we present the design and implementation of DrIveSCOVER, a recommender system for places and events in case of an in-car use, where the driving conditions such as weather and local traffic are taken into account. We integrate multiple data sources using semantic technologies and we devise recommending functions that are presented in a web-based application. Data is organized according to five root classes: accommodation, car amenity, events, gastronomy and points of interest. An interest score is calculated from the weighted user inputs in terms of preferences of classes and driving conditions. The application is available at http://drivescover.eurecom.fr/.

Document Bibtex

Titre:DrIveSCOVER: A tourism recommender system based on external driving factors
Type:Poster / Demo
Langue:English
Ville:Vienna
Pays:AUTRICHE
Date:
Département:Data Science
Eurecom ref:5302
Copyright: © ACM, 2017. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ISWC 2017, 16th International Semantic Web Conference, Poster Track, October 21-25, 2017, Vienna, Austria
Bibtex: @poster / demo{EURECOM+5302, year = {2017}, title = {{D}r{I}ve{SCOVER}: {A} tourism recommender system based on external driving factors}, author = {{K}lotz, {B}enjamin and {L}isena, {P}asquale and {T}roncy, {R}apha{\"e}l and {W}ilms, {D}aniel and {B}onnet, {C}hristian}, number = {EURECOM+5302}, month = {10}, institution = {Eurecom} address = {{V}ienna, {AUTRICHE}}, url = {http://www.eurecom.fr/publication/5302} }
Voir aussi: