Predicting and Understanding Human Choices using PCMC-Net with an application to Airline Itineraries

Channel:
Canada LLMs Explained - Aggregate Intellect - AI.SCIENCE
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Published on ● Video Link: https://www.youtube.com/watch?v=NnNSp5Gepf0


Duration: 1:00:34
272 views
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Speaker(s): Alix Lheritier
Facilitator(s): Omar Nada

Find the recording, slides, and more info at https://ai.science/e/predicting-and-understanding-human-choices-using-pcmc-net-with-an-application-to-airline-itineraries--T7VHeDI6OAv0cXM7HWYT

Motivation / Abstract
The work focuses on predicting human decisions in a setting that exhibits context effects and behaviors strongly dependent on market segments such as a complex case of airline itinerary booking. Pairwise Choice Markov Chains may come handy in this specific scenario. However, when examples of alternatives are scarce, overfitting is a common phenomenon. Additionally, the class is inappropriate when new alternatives are present in the test set. PMCN-Net proposes an amortized inference approach for PCMC based on a NN that uses the alternatives' and individuals' features to determine the transition rates.

What was discussed?
- How can the model be scalable to different domains of human behavior (decisions)?
- Is a similar model suitable for non-session based data?
- What are the main advantages of the model properties of non-regularity, non-parametric limit, and contractibility?
- How can the work be used for the recommender system?


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Tags:
deep learning
machine learning