TY - GEN
T1 - Local tours recommendation applying machine learning in social networks
AU - Medina, Braulio
AU - Pineda, Alejandro
AU - Guzmán, Giovanni
AU - Garay Jimenez, Laura Ivoone
AU - Rivera, Miguel Félix Mata
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - Tourism in Mexico represents a primarily strategic activity for the country’s economy. Although the most renowned tourist spots generally have a wide promotion of their attractions, there are local businesses that are not sufficiently linked to this benefit and it is harder for the visitors to find them. Furthermore, social media is rich not only in reactions and opinions, but in experiences, these experiences can be useful to promote and recommend several sites. Web pages also have comments, similar to TripAdvisor, but small businesses are not present in it. In this sense, the techniques of Machine Learning can help to detect businesses with a good experience reflected in comments from web sites, in combination with data from social networks. Therefore, evaluating the quality of the tourist’s experience present in comments on social networks, and gathering personal information from apps represent an opportunity to generate not only recommendations, but itineraries based on time, space and experience. In this paper we present a framework to generate itineraries based on experiences of tourists in Mexico city, using machine learning and social mining, the results show similar performance for small-local business compared with the recommendations of popular and larger places.
AB - Tourism in Mexico represents a primarily strategic activity for the country’s economy. Although the most renowned tourist spots generally have a wide promotion of their attractions, there are local businesses that are not sufficiently linked to this benefit and it is harder for the visitors to find them. Furthermore, social media is rich not only in reactions and opinions, but in experiences, these experiences can be useful to promote and recommend several sites. Web pages also have comments, similar to TripAdvisor, but small businesses are not present in it. In this sense, the techniques of Machine Learning can help to detect businesses with a good experience reflected in comments from web sites, in combination with data from social networks. Therefore, evaluating the quality of the tourist’s experience present in comments on social networks, and gathering personal information from apps represent an opportunity to generate not only recommendations, but itineraries based on time, space and experience. In this paper we present a framework to generate itineraries based on experiences of tourists in Mexico city, using machine learning and social mining, the results show similar performance for small-local business compared with the recommendations of popular and larger places.
UR - http://www.scopus.com/inward/record.url?scp=85096575645&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-62554-2_31
DO - 10.1007/978-3-030-62554-2_31
M3 - Contribución a la conferencia
AN - SCOPUS:85096575645
SN - 9783030625535
T3 - Communications in Computer and Information Science
SP - 428
EP - 440
BT - Telematics and Computing - 9th International Congress, WITCOM 2020, Proceedings
A2 - Mata-Rivera, Miguel Félix
A2 - Zagal-Flores, Roberto
A2 - Barria-Huidobro, Cristian
PB - Springer Science and Business Media Deutschland GmbH
T2 - 9th International Congress on Telematics and Computing, WITCOM 2020
Y2 - 2 November 2020 through 6 November 2020
ER -