Attention-based neural re-ranking approach for next city in trip recommendations

TL;DR

This paper presents an attention-based neural re-ranking method for next city recommendations in travel systems, combining heuristic candidate selection with transformer-inspired neural ranking, achieving competitive results in a challenge.

Contribution

It introduces a novel two-stage approach integrating heuristic selection and transformer-based neural re-ranking for city recommendation tasks.

Findings

Achieved 0.555 accuracy@4 on the challenge dataset.

Placed 5th in the Booking.com recommendations challenge.

Demonstrated effectiveness of attention-based models in recommendation re-ranking.

Abstract

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.