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Using Deep Learning to Win the Booking.com WSDM WebTour21 Challenge on Sequential Recommendations

In this paper we present our 1st place solution of the WSDM WebTour 21 Challenge. The competition task was to predict the city of the last booked hotel in a user trip, based on the previously visited cities. For our final solution, we designed three deep learning architectures based on Multilayer Perceptron (MLP), Gated Recurrent Units (GRU) and XLNet (Transformer) building blocks. A shared component among the architectures was a Session-based Matrix Factorization head (SMF), which learns a linear mapping between the item (city) embeddings and the session (trip) embeddings to generate recommendations by a dot product operation. Our final leaderboard result was 0.5939 for precision@4 and scored 2.8% better than the second solution. We published our implementation, using RAPIDS cuDF, TensorFlow and PyTorch, on github.com1.