Abstract
The advent of mobile scenario-based consumption popularizes and gradually maturates the application of point of interest (POI) recommendation services based on geographical location. However, the insufficient fusion of heterogeneous data in the current POI recommendation services leads to poor recommendation quality. In this paper, we propose a novel hybrid POI recommendation model (NHRM) based on user characteristics and spatial-temporal factors to enhance the recommendation effect. The proposed model contains three sub-models. The first model considers user preferences, forgetting characteristics, user influence, and trajectories. The second model studies the impact of the correlation between the locations of POIs and calculates the check-in probability of POI with the two-dimensional kernel density estimation method. The third model analyzes the influence of category of POI. Consequently, the above results were combined and top-K POIs were recommended to target users. The experimental results on Yelp and Meituan data sets showed that the recommendation performance of our method is superior to some other methods, and the problems of cold-start and data sparsity are alleviated to a certain extent.
Highlights
With the prevalence of smart mobile devices, location-based social networks (LBSNs) such as Foursquare, Gowalla, and Yelp have grown rapidly and become increasingly popular in recent years [1]
The personalized point of interest (POI) recommendation services integrated with situational stimulation based on mining and analyzing users’ massive check-in information, comments, and relevant behavior are favored by service providers
Our proposed method improves the accuracy of POI recommendation and is superior to some other POI recommendation methods, it suffers from two limitations
Summary
With the prevalence of smart mobile devices, location-based social networks (LBSNs) such as Foursquare, Gowalla, and Yelp have grown rapidly and become increasingly popular in recent years [1]. These platforms have offered users a way to share their life experiences in the form of a check-in. A large amount of user movement information can be obtained from the LBSNs, which provide a great opportunity to better analyze user behavior and preferences for a point of interest (POI) [2,3]. The personalized POI recommendation services integrated with situational stimulation based on mining and analyzing users’ massive check-in information, comments, and relevant behavior are favored by service providers. In order to better increase user experience in mobile scenarios, the industry and academia focus on how to improve the quality of POI
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