Hotel Recommendation System
Description
Researching and choosing a hotel for a vacation or business trip has become increasingly complex as there are too many options. As a result, the development of custom hotel recommendation systems to meet traveler’s specific preferences has been in high demand in the hospitality industry in recent years.
In 2013, one of our software engineers participated in a Kaggle competition to create the best recommendation algorithm for Expedia Travel, an American travel technology company. Out of over 2,000 Data Science teams, we secured third place. This achievement highlights our deep expertise in creating effective recommendation algorithms and the exceptional skills of our team.
Challenge
One of the main challenges was that Expedia initially 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 potential travelers with personalized hotel recommendations based on their preferences.
Solution
To develop a high-performing hotel recommendation algorithm, we used Expedia’s dataset containing information on their traveler’s online behavior, search criteria, viewed booking offers, and booked hotels over the years. Using this data, we came up with an algorithm that generated personalized hotel recommendations for travelers based on their specific needs and preferences.
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