A joint context-aware embedding for trip recommendations

Abstract

© 2019 IEEE. Trip recommendation is an important location-based service that helps relieve users from the time and efforts for trip planning. It aims to recommend a sequence of places of interest (POIs) for a user to visit that maximizes the user's satisfaction. When adding a POI to a recommended trip, it is essential to understand the context of the recommendation, including the POI popularity, other POIs co-occurring in the trip, and the preferences of the user. These contextual factors are learned separately in existing studies, while in reality, they jointly impact on a user's choice of POI visits. In this study, we propose a POI embedding model to jointly learn the impact of these conte..