With the continuous accumulation of users' check-in data, we can gradually capture users' behavior patterns and mine users' preferences. Based on this, the next point-of-interest (POI) recommendation has attracted considerable attention. Its main purpose is to simulate users' behavior habits of check-in behavior. Then, different types of context information are used to construct a personalized recommendation model. However, the users' check-in data are extremely sparse, which leads to low performance in personalized model training using recurrent neural network. Therefore, we propose a category-aware gated recurrent unit (GRU) model to mitigate the negative impact of sparse check-in data, capture long-range dependence between user check-ins and get better recommendation results of POI category. We combine the spatiotemporal information of check-in data and take the POI category as users' preference to train the model. Also, we develop an attention-based category-aware GRU (ATCA-GRU) model for the next POI category recommendation. The ATCA-GRU model can selectively utilize the attention mechanism to pay attention to the relevant historical check-in trajectories in the check-in sequence. We evaluate ATCA-GRU using a real-world data set, named Foursquare. The experimental results indicate that our ATCA-GRU model outperforms the existing similar methods for next POI recommendation.
Bibliographical noteFunding Information:
This study was supported by the National Natural Science Foundation of China (No. 61872219), the Natural Science Foundation of Shandong Province (ZR2019MF001), the Open Project of State Key Laboratory for Novel Software Technology (No. KFKT2020B08), the Macao Science and Technology Development Fund under Macao Funding Scheme for Key R&D Projects (0025/2019/AKP) and the Fundamental Research Funds for the Central Universities under Grant (No. 30919011282).
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- gated recurrent unit
- next POI recommendation