Location-based social network is rising recent years with the development of mobile internet, and point-of-interest (POI) recommendation is a hot topic of this field. Because the factors that affect the behavior of users are very complex, most of the research focuses on the context of the recommendation. But overall context data acquisition in practice is often difficult to obtain. In this paper, we have considered the most common collaborative recommendation algorithm based on user similarity, and discussed several methods of user similarity definition. Comparing the effect of different methods in the actual dataset, experimental results show among the factors including that social relation, check-in and geographical location the check-in is extremely important, so this work is of certain guiding significance to the actual applications.
Tang, Ning; Lin, Jiangyi; Weng, Wei; and Zhu, Shunzhi, "Point-of-Interest Recommendation Algorithm Based on User Similarity in Location-Based Social Networks" (2016). ICEB 2016 Proceedings. 24.