A novel recommendation system in location-based social networks using distributed ELM
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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.
KeywordsLocation-based social networks Recommendation systems Distributed ELM Large-scale recommendation
This research is partially supported by the National Natural Science Foundation of China Under Grant Nos. 61672145, 61572121, 61602323, and U1401256, and the China Postdoctoral Science Foundation Under Grant No. 2016M591455.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no potential conflicts of interest.
Human and animals participants
This article does not contain any studies involving human participants and/or animals by any of the authors.
Informed consent was obtained from all individual participants.
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