  Travel providers such as airlines and on-line travel agents are becoming more
and more interested in understanding how passengers choose among alternative
itineraries when searching for flights. This knowledge helps them better
display and adapt their offer, taking into account market conditions and
customer needs. Some common applications are not only filtering and sorting
alternatives, but also changing certain attributes in real-time (e.g., changing
the price). In this paper, we concentrate with the problem of modeling air
passenger choices of flight itineraries. This problem has historically been
tackled using classical Discrete Choice Modelling techniques. Traditional
statistical approaches, in particular the Multinomial Logit model (MNL), is
widely used in industrial applications due to its simplicity and general good
performance. However, MNL models present several shortcomings and assumptions
that might not hold in real applications. To overcome these difficulties, we
present a new choice model based on Pointer Networks. Given an input sequence,
this type of deep neural architecture combines Recurrent Neural Networks with
the Attention Mechanism to learn the conditional probability of an output whose
values correspond to positions in an input sequence. Therefore, given a
sequence of different alternatives presented to a customer, the model can learn
to point to the one most likely to be chosen by the customer. The proposed
method was evaluated on a real dataset that combines on-line user search logs
and airline flight bookings. Experimental results show that the proposed model
outperforms the traditional MNL model on several metrics.
