Abstract：The extreme sparsity of data limits the recommendation performance of the model in point-of-interest (POI) recommendation task. And the existing work ignores the differences of users' movement in different time periods. To solve the above problems, this paper proposes a POI recommendation model that incorporates time and geographical information. Firstly, the model learns multiple factors through recurrent neural network. Then the geographical relationship module is used to capture the geographical influence in the trajectory. Finally, through a unified framework, different POIs are recommended according to the different visit needs of users on weekdays and holidays. Experimental results demonstrate that the proposed model achieves better recommendation performances than the state-of-the-art methods.
赵薇, 李建波, 吕志强, 董传浩. 融合时间和地理信息的兴趣点推荐研究[J]. 复杂系统与复杂性科学, 2022, 19(4): 25-31.
ZHAO Wei, LI Jianbo, LÜ Zhiqiang, DONG Chuanhao. Research on Point-of-interest Recommendation Incorporating Time and Geographical Information. Complex Systems and Complexity Science, 2022, 19(4): 25-31.
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