Research Area:  Machine Learning
Point of interest (POI) recommendation is of great value for both service providers and users. However, it is hard due to data scarcity. To this end, in this paper, we propose a transfer learning based deep neural model, which fuses valuable cross-domain knowledge to achieve more accurate POI recommendation. We first learn the users spatial and non-spatial preferences based on their historical POI interactions. The model further captures user interactions in other domains and introduces useful preferences into POI recommendations, which can address data sparsity problems. Compared to the matrix factorization based cross-domain techniques, our method utilizes deep transfer learning, which can learn complex user-item interaction relationships and accurately capture user general preferences to transfer. Finally, we evaluate the proposed model using three real-world datasets. The experimental results show that our model significantly outperforms the state-of-the-art approaches for POI recommendation.
Author(s) Name:  Hao Zhang, Siyi Wei, Xiaojiao Hu, Ying Li & Jiajie Xu
Journal name:  Distributed and Parallel Databases volume
Publisher name:  Springer
Paper Link:   https://link.springer.com/article/10.1007/s10619-020-07299-7