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A Jointly Learned Context-Aware Place of Interest Embedding for Trip Recommendations by He, Jiayuan; Qi, Jianzhong; Ramamohanarao, Kotagiri is a scholarly article available to read on EtoBox.

What is A Jointly Learned Context-Aware Place of Interest Embedding for Trip Recommendations about?

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 impact jointly on a user's choice of a POI to visit. In this study, we propose a POI embedding model to jointly learn the impact of these contextual factors. We call the learned POI embedding a context-aware POI embedding. To showcase the effectiveness of this embedding, we apply it to generate trip recommendations given a user and a time budget. We propose two trip recommendation algorithms based on our context-aware POI embedding. The first algorithm finds the exact optimal trip by transforming and solving the trip recommendation problem as an integer linear programming problem. To achieve a high computation efficiency, the second algorithm f

Author

He, Jiayuan; Qi, Jianzhong; Ramamohanarao, Kotagiri

Published

2018

Language

EN