In order to provide personalized recommendations for travel search queries to online customers, an appropriate segmentation of customers is required using information from the search query. Clustering ensemble approaches have been 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 from different algorithmic configurations to generate more robust consensus clusters corresponding to agreements between initial clusters. We present a new clustering ensemble multi-objective optimization-based framework developed to improve personalized recommendations generated by the flight search engine of the company Amadeus. This framework optimizes diversity in the clustering ensemble search space and automatically determines an appropriate number of clusters without requiring any user input. Experimental results compare the efficiency of this approach with other existing approaches on Amadeus customer flight search data in terms of the Adjusted Rand Index and a business metric defined and used by the company.