# Combination of Diverse Ranking Models for Personalized Expedia Hotel Searches

arXiv: https://arxiv.org/abs/1311.7679 · HTML: https://mlsanity.com/paper/1311.7679/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2013.

## Built on by (most cited first, 1 of 1)

- [EvoRank: LLM-Guided Evolution of Multi-Objective Learning-to-Rank Pipelines](https://mlsanity.com/paper/2609.22196/) (2026)

## Abstract (arXiv)

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.