A Hybrid Recommender Model based on Information Retrieval for Mexican Tourism Text in Rest-Mex 2022

ABSTRACT

Nowadays, the tourism is a principal economic sector for the world due to the exportations are improved, the jobs number is enhanced and the economic is developed. In México, the tourism represents 8.7% of GDP and generates 4.5 million direct jobs, however this economic sector has been affected by COVID-19 pandemic. For these reasons, a hybrid recommender model based on information retrieval is presented in this research to tackle the recommendation systems task of Rest-Mex 2022. A vector space model with tf-idf weighting scheme and cosine similarity is implemented. Besides, a hybrid recommender model is generated applying the recommendation techniques item-item collaborative filtering, content-based filtering and switching hybrid approach. Finally, our proposal won the second and third place in the competition. © 2022 Copyright for this paper by its for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).