Hotel Recommendation System

TL;DR

This paper presents a hotel recommendation system that predicts the top five hotel clusters a user is likely to book, utilizing Expedia's dataset to understand user preferences and improve online hotel booking experiences.

Contribution

The study develops a hotel recommendation model using Expedia data to accurately predict user preferences among multiple hotel clusters.

Findings

Achieved high accuracy in predicting user hotel choices

Demonstrated effectiveness of feature engineering in recommendation accuracy

Provided insights into factors influencing hotel selection

Abstract

One of the first things to do while planning a trip is to book a good place to stay. Booking a hotel online can be an overwhelming task with thousands of hotels to choose from, for every destination. Motivated by the importance of these situations, we decided to work on the task of recommending hotels to users. We used Expedia's hotel recommendation dataset, which has a variety of features that helped us achieve a deep understanding of the process that makes a user choose certain hotels over others. The aim of this hotel recommendation task is to predict and recommend five hotel clusters to a user that he/she is more likely to book given hundred distinct clusters.