A Comprehensive Pipeline for Hotel Recommendation System
This is an incremental improvement for hotel recommendation systems, applying existing methods to new smartphone data.
The paper tackled building a hotel recommendation system using raw smartphone app data by implementing a pipeline with pre-processing and training SVM and RNN models, achieving reasonable accuracy.
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.