Personalized Recommendation Method of POI Based on Deep Neural Network With the rapid development of Locationbased social networks (LBSN), there is a growing demand for location services. How to use the usersu0027 historical checkin data for exploring their visit patterns and preference characteristics to realize personalized pointofinterest (POI) recommendation has become an important topic. Finding valid features from the checkin data is the key to POI recommendation. Deep learning is a multilevel representation learning method, which can better explore the relationship between features. Therefore, a new POI recommendation model named DLM based on deep neural network is proposed in this paper. This model incorporates topic features, user preference features and geographical factor features in the LBSN into the POI recommendation tasks, thereby it improves the efficiency of usersu0027 personalized POI recommendation. A lot of experiments on public data set Foursquare have proved the advantages and effectiveness of the proposed method.

Discovering Functional Regions in Modern Cities by Using User Checkin Records and POIs The rapid development of the modern city necessitates the division of different functional regions, such as educational regions and business regions. In this paper, we propose an LDA based topic model to discover regions with different functions in a city using user checkin records and PointofInterests (POIs). We infer the functions of each region using the proposed model, which regards a region as a document, a function as a topic, categories of POIs and checkin frequency as words. As a result, a region is represented by a distribution of functions, and a function is represented by the distribution of POI categories. We further identify the intensity to illustrate that our method considers both the spatial distance and checkin frequency. We evaluate our method by using a large scale dataset, consisting of two user checkin records that occurred in Los Angeles and Lower Manhattan. The experiment results justify the advantages of our proposed method over the baseline method solely using POIs or checkin records.