Predicting your next trip: A knowledge graph-based multi-task learning approach for travel destination recommendation

Dadoun, Amine; Troncy, Raphaël; Defoin Platel, Michael; Ayala Solano, Gerardo

Recommending the next destination to a traveler is a task that has been at the forefront of the airline industry for a long time, and its relevance has never been more important than today to revive tourism after the Covid-19 crisis. Several factors influence a user’s
decision when faced with a variety of travel destination choices: geographic context, best time to go, personal experiences, places to visit, scheduled events, etc. The challenge of recommending the right travel destination lies in efficiently integrating and leveraging
all of this information into the recommender system. Based on a real world application scenario, we propose a multi-task learning model based on a neural network architecture that leverages knowledge graph to recommend the next destination to a traveler. We
experimentally evaluated our proposed approach by comparing it against the currently in-production system and state-of-the-art travel destination recommendation algorithms in an offline setting. The results confirm the significant contribution of using knowledge
graphs as a means of representing the heterogeneous information used for the recommendation task, as well as the benefit of using a multi-task learning model in terms of recommendation performance and training time.

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