Recommendations based on user effective point-of-interest path

作     者：Zhou, Guoqiang Zhang, Shuai Fan, Yi Li, Jingjin Yao, Wenbo Liu, Hongfang 

作者机构：Nanjing Univ Posts & Telecommun Coll Comp Sci Nanjing 210003 Jiangsu Peoples R China 

出 版 物：《INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS》 (国际机器学习与控制论杂志)

年 卷 期：2019年第10卷第10期

页      面：2887-2899页

核心收录：

学科分类：08[工学] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 081102[工学-检测技术与自动化装置] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Location-based social networks Effective path coverage Recommendation algorithm Point-of-interest 

摘      要：Point-of-interest (POI) recommendation has become an important service in location-based social networks. Existing recommendation algorithms provide users with a diverse pool of POIs. However, these algorithms tend to generate a list of unrelated POIs that user cannot continuously visit due to lack of appropriate associations. In this paper, we first proposed a concept that can recommend POIs by considering both category diversity features of POIs and possible associations of POIs. Then, we developed a top-k POI recommendation model based on effective path coverage. Moreover, considering this model has been proven to be a NP-hard problem, we developed a dynamic optimization algorithm to provide an approximate solution. Finally, we compared it with two popular algorithms by using two real-world datasets, and found that our proposed algorithm has better performance in terms of diversity and precision.