基于语义位置和区域划分的兴趣点推荐模型

ccording to the existing research work of location-based social network was not sufficient to mine the user semantic location information related to point-of-interest, Moreover, most recommendation algorithms ignored the influence of the region of point-of-interest on the result of recommendation. This paper proposed a new recommendation model of point-of-interest called USTTGD, first adopted the Latent Dirichlet Allocation(LDA) topic model of time division, based on the semantic location information in check-in records mined the user time preference under the time theme, then devided the region of point-of-interest into grids to evaluate the regional influence. Next, applied Edge-weighted Personalized PageRank(EwPPR) to modeling the successive transitions among point-of-interests. Finally, USTTGD fused user time preference, regional preference and successive transition preference into a unified recommendation framework. Experimental results on real-world datasets show that USTTGD achieves significantly enhance compared with other classical recommendation models on precision and recalling rates.