3 research outputs found
Population Density-based Hospital Recommendation with Mobile LBS Big Data
The difficulty of getting medical treatment is one of major livelihood issues
in China. Since patients lack prior knowledge about the spatial distribution
and the capacity of hospitals, some hospitals have abnormally high or sporadic
population densities. This paper presents a new model for estimating the
spatiotemporal population density in each hospital based on location-based
service (LBS) big data, which would be beneficial to guiding and dispersing
outpatients. To improve the estimation accuracy, several approaches are
proposed to denoise the LBS data and classify people by detecting their various
behaviors. In addition, a long short-term memory (LSTM) based deep learning is
presented to predict the trend of population density. By using Baidu
large-scale LBS logs database, we apply the proposed model to 113 hospitals in
Beijing, P. R. China, and constructed an online hospital recommendation system
which can provide users with a hospital rank list basing the real-time
population density information and the hospitals' basic information such as
hospitals' levels and their distances. We also mine several interesting
patterns from these LBS logs by using our proposed system
Joint modeling of users' interests and mobility patterns for point-of-interest recommendation
Point-of-Interest (POI) recommendation has become an important means to help people discover interesting places, especially when users travel out of town. However, extreme sparsity of user-POI matrix creates a severe challenge. To cope with this challenge, we propose a unified probabilistic generative model, Topic-Region Model (TRM), to simultaneously discover the semantic, temporal and spatial patterns of users' check-in activities, and to model their joint effect on users' decision-making for POIs. We conduct extensive experiments to evaluate the performance of our TRM on two real large-scale datasets, and the experimental results clearly demonstrate that TRM outperforms the state-of-art methods