Paper Info
Title
Hybrid Markov Location Prediction Algorithm Based On Dynamic Social Ties
Abstract
Much research which has shown the usage of social ties could improve the location predictive performance, but as the strength of social ties is varying constantly with time, using the movement data of user's close friends at different times could obtain a better predictive performance. A hybrid Markov location prediction algorithm based on dynamic social ties is presented. The time is divided by the absolute time (week) to mine the long-term changing trend of users' social ties, and then the movements of each week are projected to the workdays and weekends to find the changes of the social circle in different time slices. The segmented friends' movements are compared to the history of the user with our modified cross-sample entropy to discover the individuals who have the relatively high similarity with the user in different time intervals. Finally, the user's historical movement data and his friends' movements at different times which are assigned with the similarity weights are combined to build the hybrid Markov model. The experiments based on a real location-based social network dataset show the hybrid Markov location prediction algorithm could improve 15% predictive accuracy compared with the location prediction algorithms that consider the global strength of social ties.
Year
DOI
Venue
2015
10.1587/transinf.2014EDP7296
IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
Keywords
Field
DocType
location prediction, dynamic social ties, hybrid Markov model, cross-sample entropy
Maximum-entropy Markov model,Pattern recognition,Markov model,Computer science,Markov chain,Artificial intelligence,Variable-order Markov model,Location prediction,Machine learning,Interpersonal ties
Journal
Volume
Issue
ISSN
E98D
8
1745-1361
Citations 
PageRank 
References 
2
0.38
11
Authors
4
Name
Order
Citations
PageRank
Wen Li141.11
Shixiong Xia210213.28
Feng Liu348.12
Lei Zhang425514.13