Combination of Diverse Ranking Models for Personalized Expedia Hotel Searches
This work addresses hotel booking optimization for online travel agencies, but it is incremental as it applies existing ensemble methods to a competition dataset.
The paper tackled the problem of personalized hotel ranking for Expedia searches by combining diverse ranking models from team members, achieving a top performance in the ICDM 2013 challenge.
The ICDM Challenge 2013 is to apply machine learning to the problem of hotel ranking, aiming to maximize purchases according to given hotel characteristics, location attractiveness of hotels, user's aggregated purchase history and competitive online travel agency information for each potential hotel choice. This paper describes the solution of team "binghsu & MLRush & BrickMover". We conduct simple feature engineering work and train different models by each individual team member. Afterwards, we use listwise ensemble method to combine each model's output. Besides describing effective model and features, we will discuss about the lessons we learned while using deep learning in this competition.