Cascaded machine learning for increasing conversion in hospitality recommender system

González Ferrer, Antonio Javier (2018). Cascaded machine learning for increasing conversion in hospitality recommender system. Thesis (Master thesis), E.T.S. de Ingenieros Informáticos (UPM).

Description

Title: Cascaded machine learning for increasing conversion in hospitality recommender system
Author/s:
  • González Ferrer, Antonio Javier
Contributor/s:
  • Håkansson, Anne
  • Vlassov, Vladimir
Item Type: Thesis (Master thesis)
Masters title: Data Science
Date: 3 October 2018
Subjects:
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Otro
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Recommender systems refer to algorithms widely used in industry to determine the preferred product to propose to a customer, given some information about the customer and the context of the purchase. In this thesis, such an approach is applied to predict the desirability of hotels given information about an air travel booking. Specifically, we present a novel recommender system which optimizes the booking conversion based on a list of hotels chosen from a larger set. The proposed solution uses information such as details about the associated flight booking, characteristics of each hotel and the attributes of the list of hotels proposed. The main contribution of this thesis concerns the Hotel List Builder (HLB) which is the component of the recommender system that generates the new recommendations of hotels. This component relies on a two-stage machine learning model and the feature importance analysis of the hotel bookings. The expected conversion rate is improved from 0.049% to 0.186% on average due to the new recommendation system. This method also results in a significant improvement in the processing time when the HLB is applied with respect to a brute force solution to build an optimal list of hotel recommendations (up to 20 times faster).

More information

Item ID: 56662
DC Identifier: http://oa.upm.es/56662/
OAI Identifier: oai:oa.upm.es:56662
Deposited by: Biblioteca Facultad de Informatica
Deposited on: 03 Oct 2019 08:28
Last Modified: 03 Oct 2019 08:28
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