Attention-based neural re-ranking approach for next city in trip recommendations
This work addresses trip recommendations for travel systems, but it is incremental as it adapts existing methods like transformers to a specific domain.
The paper tackles the next destination city recommendation problem for travel reservations by proposing a two-stage approach with heuristic candidate selection and an attention neural network for re-ranking, achieving 0.555 accuracy@4 on a closed dataset and placing 5th in the Booking.com challenge.
This paper describes an approach to solving the next destination city recommendation problem for a travel reservation system. We propose a two stages approach: a heuristic approach for candidates selection and an attention neural network model for candidates re-ranking. Our method was inspired by listwise learning-to-rank methods and recent developments in natural language processing and the transformer architecture in particular. We used this approach to solve the Booking.com recommendations challenge Our team achieved 5th place on the challenge using this method, with 0.555 accuracy@4 value on the closed part of the dataset.