Kaggle Competition: Expedia Hotel Recommendations

TL;DR

This paper discusses a Kaggle competition where the goal was to develop a model to predict hotel preferences for Expedia users, aiming to improve personalized hotel recommendations based on customer data.

Contribution

The work introduces a predictive modeling approach for hotel recommendation personalization using customer data, addressing data limitations for individual customization.

Findings

Developed a model predicting user hotel preferences with measurable accuracy.

Enhanced understanding of customer behavior patterns in hotel selection.

Provided a benchmark for future hotel recommendation systems.

Abstract

With hundreds, even thousands, of hotels to choose from at every destination, it's difficult to know which will suit your personal preferences. Expedia wants to take the proverbial rabbit hole out of hotel search by providing personalized hotel recommendations to their users. This is no small task for a site with hundreds of millions of visitors every month! Currently, Expedia uses search parameters to adjust their hotel recommendations, but there aren't enough customer specific data to personalize them for each user. In this project, we have taken up the challenge to contextualize customer data and predict the likelihood a user will stay at 100 different hotel groups.