结合动态多类信息的兴趣点推荐

利用用户的历史签到数据的同时考虑用户的长期偏好和短期偏好已成为当今兴趣点（Point-of-Interest, POI）推荐的主流方法之一。然而，现有方法往往忽略了用户评论中隐含的用户偏好信息，忽视了不同用户在对长期偏好和短期偏好的依赖上所存在的差异性。针对上述局限，本文提出一种结合动态多类信息的兴趣点推荐方法DMGCR。首先，利用注意力机制捕获用户对不同兴趣点的关注程度，定量刻画用户对兴趣点的长期偏好。其次，将评论信息与已有的位置和类别信息相结合，并利用双向长短期记忆网络学习评论文本中隐含的语义特征，在捕获用户对兴趣点情感倾向的基础上准确刻画用户的短期偏好。最后，设计融合动态多类信息的用户偏好综合预测函数，实现下一个兴趣点推荐概率的定量计算。多个数据集上的实验结果验证了该方法在推荐性能上的有效性和优越性。

Point of Interest Recommendation Combined with Dynamic Multiple Types of Information

For the current mainstream point-of-interest recommendation algorithm, on the one hand, the user’s historical check-in data needs to be used, and on the other hand, the user’s long-term and short-term preferences need to be considered at the same time. However, existing methods tend to ignore the user preference information implied in user reviews and ignore the differences in the dependence of different users on long-term and short-term preferences. In view of the above limitations, a method of POI recommendation combined with dynamic multiple types of information （DMGCR） is proposed. Firstly, the attention mechanism is used to capture the user’s attention to different POI, so as to quantitatively describe the user’s long-term preference for POI. Secondly, the review information is combined with location and category information, and Bi-directional Long-Short Term Memory is used to learn the semantic features implicit in the review text. In this way, the user’s short-term preferences can be accurately portrayed on the basis of capturing the user’s emotional tendency toward POI. Finally, a comprehensive prediction function of user preferences integratedynamic multiple types of information is designed. Then the quantitative calculation of the recommendation probability of the next POI can be realized. Experimental results on multiple data sets verified the effectiveness and superiority of this method in recommendation performance.