GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Random walk with restart: fast solutions and applications
Knowledge and Information Systems
Location recommendation for location-based social networks
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Improving location recommendations with temporal pattern extraction
Proceedings of the 18th Brazilian symposium on Multimedia and the web
Proceedings of the Fifth International Conference on Management of Emergent Digital EcoSystems
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An on-demand bus is like a shared taxi that operates only when riders want to travel between the origin and destination locations. It offers many advantages over fixed-route buses, but the riders are bothered by the need to tediously enter such data as origins, destinations, and deadlines. A location recommendation system that predicts such data would help riders during the reservation process and help target potential riders when buses are idle. In this paper, a general and scalable framework for such location recommendation algorithms is presented. It is based on users' location histories and spatio-temporal correlations among the locations by combining prediction methods of the collaborative filtering algorithms, which are widely used in e-commerce, with a popular method in data mining called link propagation. Experiments on real-world data demonstrate that the accuracy of recommendations with the spatio-temporal information is better than those without.