A novel recommendation system in location-based social networks using distributed ELM

Location-based social networks (LBSNs) have become a popular platform for people to communicate with each other. The recommendation problem has attracted considerable attention in both academia and industry as increasingly more users share their experiences and feelings using LBSNs. Machine learning has been widely used in many recommendation systems for recommending new friends or places of interest (POIs) to users in LBSNs. However, the majority of the existing recommendation systems were single function and only used small-scale datasets to provide recommendation services. In the era of big data, recommendation systems should have the ability to fully utilize limited computing resources for mining potential relationships from large-scale LBSN data. In this paper, a novel generic recommendation system is proposed by utilizing a distributed extreme learning machine called GR-DELM, which considers both friend recommendation and POI recommendation in large-scale datasets. For POI recommendation, three features are extracted: (1) geography-influenced feature, (2) popularity-influenced feature, and (3) social-influenced feature. For friend recommendation, two features are extracted: (1) neighborhood-based feature and (2) path-based feature. These features further improve the efficiency and accuracy of large-scale recommendation. Finally, a series of experiments demonstrate that the GR-DELM system outperforms the existing recommendation systems.