Kaggle Competition: Expedia Hotel Recommendations
This addresses the challenge of personalizing hotel recommendations for a site with hundreds of millions of monthly visitors, though it appears incremental as it builds on existing search parameters.
The project tackled the problem of providing personalized hotel recommendations for Expedia users by predicting the likelihood of a user staying at 100 different hotel groups, using contextualized customer data to address the lack of user-specific information.
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.