Ecole d'ingénieur et centre de recherche en Sciences du numérique

Multi-objective consensus clustering framework for flight search recommendation

Chatterjee, Sujoy; Pasquier, Nicolas; Nanty, Simon; Zuluaga, Maria A

Submitted to ArXiV, 20 February 2020

In the travel industry, online customers book their travel itinerary according to several features, like cost and duration of the travel or the quality of amenities. To provide personalized recommendations for travel searches, an appropriate segmentation of customers is required. Clustering ensemble approaches were developed to overcome well-known problems of classical clustering approaches, that each rely on a different theoretical model and can thus identify in the data space only clusters corresponding to this model. Clustering ensemble approaches combine multiple clustering results, each from a different algorithmic configuration, for generating more robust consensus clusters corresponding to agreements between initial clusters. We present a new clustering ensemble multi-objective optimization-based framework developed for analyzing Amadeus customer search data and improve personalized recommendations. This framework optimizes diversity in the clustering ensemble search space and automatically determines an appropriate number of clusters without requiring user's input. Experimental results compare the efficiency of this approach with other existing approaches on Amadeus customer search data in terms of internal (Adjusted Rand Index) and external (Amadeus business metric) validations.    

Arxiv Hal Bibtex

Titre:Multi-objective consensus clustering framework for flight search recommendation
Mots Clés:Clustering Ensemble, Multi-objective optimization
Département:Data Science
Eurecom ref:6183
Copyright: © EURECOM. Personal use of this material is permitted. The definitive version of this paper was published in Submitted to ArXiV, 20 February 2020 and is available at :
Bibtex: @techreport{EURECOM+6183, year = {2020}, title = {{M}ulti-objective consensus clustering framework for flight search recommendation}, author = {{C}hatterjee, {S}ujoy and {P}asquier, {N}icolas and {N}anty, {S}imon and {Z}uluaga, {M}aria {A}}, number = {EURECOM+6183}, month = {02}, institution = {Eurecom}, url = {},, }
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