Abstract: 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.

Key words: long and short term preference, Point-of-Interest recommendation, comment information, Long Short-Term Memory, attention mechanism