Data Augmentation Using Many-To-Many RNNs for Session-Aware Recommender Systems
Information Retrieval
2021-08-27 v1 Machine Learning
Abstract
The ACM WSDM WebTour 2021 Challenge organized by Booking.com focuses on applying Session-Aware recommender systems in the travel domain. Given a sequence of travel bookings in a user trip, we look to recommend the user's next destination. To handle the large dimensionality of the output's space, we propose a many-to-many RNN model, predicting the next destination chosen by the user at every sequence step as opposed to only the final one. We show how this is a computationally efficient alternative to doing data augmentation in a many-to-one RNN, where we consider every subsequence of a session starting from the first element. Our solution achieved 4th place in the final leaderboard, with an accuracy@4 of 0.5566.
Keywords
Cite
@article{arxiv.2108.09858,
title = {Data Augmentation Using Many-To-Many RNNs for Session-Aware Recommender Systems},
author = {Martín Baigorria Alonso},
journal= {arXiv preprint arXiv:2108.09858},
year = {2021}
}