Location embeddings for next trip recommendation

Abstract

The amount of information available in social media and specialized blogs has become useful for a user to plan a trip. However, the user is quickly overwhelmed by the list of possibilities offered to him, making his search complex and time-consuming. Recommender systems aim to provide personalized suggestions to users by leveraging different type of information, thus assisting them in their decision-making process. Recently, the use of neural networks and knowledge graphs have proven to be efficient for items recommendation. In our work, we propose an approach that leverages contextual, collaborative and content information in order to recommend personalized destinations to travelers. We compare our approach with a set of state of the art collaborative filtering methods and deep learning based recommender systems.