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Combining RNN with Transformer for Modeling Multi-Leg Trips

Recommending destinations of trips based on user behavior is an important task for travel agencies such as Booking.com. The Booking.com Challenge WebTour 2021 ACMWSDM workshop is aimed at building models for this task; the goal is to predict the final destination of multi-destination trips, based on a large dataset of over a million-trip reservations at Booking.com with date, destination, etc. In this paper, I present my approach where I leverage recent advances in language modeling techniques including Transformer. The approach, which used only a sequence of the visited cities for input, showed a top-4 accuracy of 0.4720 on the final result leaderboard. Full code is available here: https://github.com/sakatani/BookingcomChallenge2021.