Rest-MEX: Predicting recommendation

Given a TripAdvisor tourist and a Mexican tourist place, the goal is to automatically predict the expected degree of satisfaction the tourist (between 1 and 5) when visiting that place.

The systems may use the following information:

  • User information: Gender (Male or female) and place of origin, the description given by the user on TripAdvisor and her opinions on other touristic destinationsr.

  • Place information: A brief text describing the place and the touristic profile (dventure, beach, relax, familiar, free activities, and so on).

Publication
Miguel Á. Álvarez-Carmona, Ángel Díaz-Pacheco, Ramón Aranda, Ansel Y. Rodríguez-González, Daniel Fajardo-Delgado, Rafael Guerrero-Rodríguez, Lázaro Bustio-Martínez (2022) Overview of Rest-Mex at IberLEF 2022: Recommendation System, Sentiment Analysis and Covid Semaphore Prediction for Mexican Tourist Texts. Procesamiento del Lenguaje Natural, Revista nº 69, septiembre de 2022, pp. 289-299.
Competition
Rest-Mex en IberLEF 2022: Sistema de Recomendación, Análisis de Sentimiento y Predicción de Semáforo Covid para Textos Turísticos Mexicanos
Language
Spanish
URL Task
https://sites.google.com/cicese.edu.mx/rest-mex-2022
NLP topic
recommendation systems
Abstract task
Regression
Dataset
RestMEX-recommendation system
Year
2022
Publication link
http://journal.sepln.org/sepl…
Ranking metric
MAE

Task results

If you have published a result better than those on the list, send a message to odesia-comunicacion@lsi.uned.es indicating the result and the DOI of the article, along with a copy of it if it is not published openly.