This paper addresses a comprehensive pipeline to build a hotel recommendation system with the raw data collected by Apps in users' smartphones. The pipeline mainly consists of pre-processing of the raw data and training prediction models. We use two methods, Support Vector Machine (SVM) and Recurrent Neural Network (RNN). The results show that two methods achieved a reasonable accuracy with the pre-processing of the raw data. Therefore, we conclude that this paper provides a comprehensive pipeline, in which a hotel recommendation system was successfully built from the raw data to specific applications.
@article{arxiv.2009.01860,
title = {A Comprehensive Pipeline for Hotel Recommendation System},
author = {J. Chen and Z. Gao},
journal= {arXiv preprint arXiv:2009.01860},
year = {2020}
}