An attention-based category-aware GRU model for the next POI recommendation

Yuwen Liu, Aixiang Pei, Fan Wang, Yihong Yang, Xuyun Zhang, Hao Wang, Hongning Dai, Lianyong Qi*, Rui Ma

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

90 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)3174-3189
Number of pages16
JournalInternational Journal of Intelligent Systems
Issue number7
Publication statusPublished - Jul 2021


  • attention
  • category&#8208
  • aware
  • embedding
  • gated recurrent unit
  • next POI recommendation
  • category-aware


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