Effective feature reduction for link prediction in location-based social networks

Date

2019-10-01

Author

Bayrak, Ahmet Engin
Polat, Faruk

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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In this study, we investigated feature-based approaches for improving the link prediction performance for location-based social networks (LBSNs) and analysed their performances. We developed new features based on time, common friend detail and place category information of check-in data in order to make use of information in the data which cannot be utilised by the existing features from the literature. We proposed a feature selection method to determine a feature subset that enhances the prediction performance with the removal of redundant features by clustering them. After clustering features, a genetic algorithm is used to determine the ones to select from each cluster. A non-monotonic and feasible feature selection is ensured by the proposed genetic algorithm. Results depict that both new features and the proposed feature selection method improved link prediction performance for LBSNs.

Subject Keywords

Library and Information Sciences, Information Systems

URI

https://hdl.handle.net/11511/41892

Journal

JOURNAL OF INFORMATION SCIENCE

DOI

https://doi.org/10.1177/0165551518808200

Collections

Department of Computer Engineering, Article

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Citation Formats

A. E. Bayrak and F. Polat, “Effective feature reduction for link prediction in location-based social networks,” JOURNAL OF INFORMATION SCIENCE, pp. 676–690, 2019, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/41892.