Abstract
The location-based social network (LBSN) contains a large amount of user check-in data informations, in order to better improve the recommendation performance and avoid the impact of user check-in data sparsity. It is proposed to mine the time-category informations in the user's check-in data to carry out network modeling; by using the belief propagation algorithm on the time-category Markov network to obtain the user's social influence set. Calculating the similarity and familiarity of the social users in the collection, linearly integrate the unified social influence factors and geographical location influences, to recommend locations. Experimental analysis in the Foursquare dataset, compared with other algorithms, the recommendation algorithm performance of combining social influence and geographic location based on belief propagation has improved.
Highlights
In recent years, with the rapid development of communication networks, mobile devices and positioning systems, more and more users sign in to a location by using mobile intelligent devices and generate sign-in information on social software
In location-based social network (LBSN), when users check-in to a location, they share location information on social platforms or smart devices, and post some comments information about the location
1) Based on the influence of time, the time is segmented, and the network modeling is carried out by mining the mutual connection of users to check-in different types of locations in the corresponding time period; 2) Based on the network using belief propagation algorithm, the solution is based on the set of users with greater influence under the location category; 3) Based on the solved user set, by calculating the similarity and trust degree of the user check-in, combined with the influence of geographical location, linearly combined to form a location recommendation list to implement recommendations
Summary
With the rapid development of communication networks, mobile devices and positioning systems, more and more users sign in to a location by using mobile intelligent devices and generate sign-in information on social software. Ye et al [3] first proposed the use of collaborative filtering methods for POI recommendation, which integrated the user’s personal preferences, social and geographic location, improved the algorithm and the recommendation results. 1) Based on the influence of time, the time is segmented, and the network modeling is carried out by mining the mutual connection of users to check-in different types of locations in the corresponding time period; 2) Based on the network using belief propagation algorithm, the solution is based on the set of users with greater influence under the location category; 3) Based on the solved user set, by calculating the similarity and trust degree of the user (location) check-in, combined with the influence of geographical location, linearly combined to form a location recommendation list to implement recommendations. This algorithm can avoid the influence of data sparsity and improve the corresponding recommendation performance, and can recommend locations for new users
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