A Comprehensive Pipeline for Hotel Recommendation System

TL;DR

This paper presents a complete pipeline for building hotel recommendation systems from raw smartphone app data, utilizing data pre-processing and machine learning models like SVM and RNN to achieve reasonable accuracy.

Contribution

It introduces a comprehensive pipeline that covers raw data pre-processing and model training for hotel recommendations, integrating SVM and RNN methods.

Findings

Both SVM and RNN achieved reasonable accuracy.

The pipeline effectively transforms raw app data into actionable recommendations.

The approach demonstrates the feasibility of end-to-end hotel recommendation systems.

Abstract

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.