Hotel Recommendation System
The process of choosing a hotel for a vacation or business trips is becoming increasingly complex. To address this challenge, an algorithm-based hotel recommendation service is highly in demand in the hotel industry.
Our team member participated in a Kaggle contest for the best recommendation service algorithm for Expedia Travel. The result was securing third-place among over 2,000 teams specializing in Data Science. This accomplishment highlights our proficiency in creating effective recommendation algorithms.
Industry:
Hospitality
Region:
USA
Technologies:
Python
Volume:
0.1 man/year
Challenge
At the launch of the project, Expedia lacked the necessary data for analytical modeling and forecasting. Therefore, one of the project objectives was to develop a data model taking into account multiple parameters. This model would later enable high-quality analytics and provide customers with personalized hotel recommendations based on their preferences.
Solution
Expedia supplied a dataset containing information on customer behavior, search criteria, viewed booking offers, and booked hotels over the years. Based on the search criteria and additional customer details, the algorithm generated a list of personalized hotel recommendations. The algorithm was given third place in the Kaggle contest.
