# Deep Choice Model Using Pointer Networks for Airline Itinerary Prediction

arXiv: https://arxiv.org/abs/1803.05976 · HTML: https://mlsanity.com/paper/1803.05976/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2018.

## Built on by (most cited first, 9 of 9)

- [A Survey on Service Route and Time Prediction in Instant Delivery: Taxonomy, Progress, and Prospects](https://mlsanity.com/paper/2309.01194/) (2023)
- [On the Capabilities of Pointer Networks for Deep Deductive Reasoning](https://mlsanity.com/paper/2106.09225/) (2021)
- [On the estimation of discrete choice models to capture irrational customer behaviors](https://mlsanity.com/paper/2109.03882/) (2021)
- [A machine learning approach to itinerary-level booking prediction in competitive airline markets](https://mlsanity.com/paper/2103.08405/) (2021)
- [Page-level Optimization of e-Commerce Item Recommendations](https://mlsanity.com/paper/2108.05891/) (2021)
- [Learning Geometric Combinatorial Optimization Problems using Self-attention and Domain Knowledge](https://mlsanity.com/paper/2107.01759/) (2021)
- [Negotiating Networks in Oligopoly Markets for Price-Sensitive Products](https://mlsanity.com/paper/2110.13303/) (2021)
- [A Data-Driven Column Generation Algorithm For Bin Packing Problem in Manufacturing Industry](https://mlsanity.com/paper/2202.12466/) (2022)
- [Pacos: Modeling Users' Interpretable and Context-Dependent Choices in Preference Reversals](https://mlsanity.com/paper/2303.05648/) (2023)

## Abstract (arXiv)

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.