Airlines have seen numerous changes in the way their offer is being structured since many years. Selling at the beginning air tickets which includes a wide selection of services, airlines are now selling significant volumes of ancillary services, ranging from flexibility options to additional comfort on board. Airlines went further by distributing as well, especially on their website, items sold by third party providers (rental cars, hotels, excursion, activities, etc.), aiming at making their offer cover the entire traveler journey.
Recommender Systems have demonstrated their huge impact when systematically applied in situations where enough data are available for Machine Learning algorithms to build accurate models. It is for example the case for the online retailing industry that has been drastically transformed with the emergence of automatic recommendations.
To fully benefit from the power of Recommender Systems, it is necessary for the airlines to identify the potential recommendation use cases and then, to implement the corresponding technologies to customize their offers. More specifically, it is crucial to address the following
points: what product to offer, to which customer, when to recommend an offer, at which price, and finally, how this offer should be presented to the customer and on which touchpoint.
The aim of this thesis is to provide answers to the aforementioned questions, to analyze the benefits of recommender systems for the airline travel industry and to propose novel recommender systems adapted to the airline industry with the objective to optimize airlines' offers conversion rate and improve the travelers experience..