An ensemble of standard classifiers plus a data-leakage lookup achieves MAP@5 of 0.496 on the Expedia hotel recommendation test set.
Combination of Diverse Ranking Models for Personalized Expedia Hotel Searches
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abstract
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.
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cs.LG 1years
2019 1verdicts
REJECT 1representative citing papers
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Hotel Recommendation System
An ensemble of standard classifiers plus a data-leakage lookup achieves MAP@5 of 0.496 on the Expedia hotel recommendation test set.