Understanding Customer Choices to Improve Recommendations in the Air Travel Industry

Recommender systems aim at suggesting relevant items to users to support them in various decision-making processes, on the basis of available information on items or users. In the latter, the customer’s interests and tastes can be learnt and expressed using historical browsing data, purchase histories, and even other nontraditional data sources such as social networks. Despite its proven success in the on-line retailing industry, in electronic commerce and, even tourism, recommender systems have been less popular in flight itinerary selection processes. This could be partially explained by the fact that customers’ interests are only expressed as a flight search request. As a result, this problem has been historically tackled using classical Discrete Choice Modelling techniques and, more recently, through the use of data-driven approaches such as Machine and Deep Learning techniques. At Amadeus, we are interested in the use of choice models with recommender systems for the problem of airline itinerary selection. This work presents a benchmark on three family of methods to identify which is the most suitable for the problem we tackle.