Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 37

The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.

Deep Choice Model Using Pointer Networks for Airline Itinerary Prediction

Mottini, Alejandro, Acuna-Agost, Rodrigo·2017·Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
DOI 10.1145/3097983.3098005
Open this paper in CoChat — free account

Abstract

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1145/3097983.3098005

Cites

41 of 41 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Research works printed but not linked: 3

  1. G.M. Coldren , F.S. Koppelman , K. Kasturirangan , and A. Mukherjee 2003. Air travel itinerary share prediction: Logit model development at a major U.S airline 82nd Annual Meeting of the Transportation Research Board . G.M. Coldren, F.S. Koppelman, K. Kasturirangan, and A. Mukherjee 2003. Air travel itinerary share prediction: Logit model development at a major U.S airline 82nd Annual Meeting of the Transportation Research Board.
  2. 10.5555/2770678.2770682 · doi:10.5555/2770678.2770682
  3. 10.3141/1951-02 · DOI

Cited by

See all 37

Metadata

Authors
Mottini, A., & Acuna-Agost, R.
Affiliations
Amadeus SAS, Nice, France
Venue
Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Publisher
ACM
Published
2017-08-13
Pages
1575-1583
DOI
10.1145/3097983.3098005
Cited by
37
References
41
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.