Deep distributed learning-based POI recommendation under mobile edge networks

Guo, Z, Yu, K, Kumar, N, Wei, W, Mumtaz, S ORCID: https://orcid.org/0000-0001-6364-6149 and Guizani, M, 2022. Deep distributed learning-based POI recommendation under mobile edge networks. IEEE Internet of Things Journal. ISSN 2327-4662

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Abstract

With the rapid development of edge intelligence in wireless communication networks, mobile edge networks (MEN) have been broadly discussed in academia. Supported by considerable geographical data acquisition ability of mobile Internet of Things (IoT), the MEN can also provide spatial locations-based social service to users. Therefore, suggesting reasonable points-of-interest (POIs) to users is essential to improve user experience of MEN. As the simple user-location data is usually sparse and not informative, existing literature attempted to extend feature space from two perspectives: contextual patterns and semantic patterns. However, previous approaches mainly focused on internal features of users, yet ignoring latent external features among them. To address this challenge, in this paper, a deep distributed learning-based POI recommendation (Deep-PR) method is proposed for situations of MEN. In particular, hidden feature components from both local and global subspaces are deeply abstracted via representative learning schemes. Besides, propagation operations are embedded to iteratively reoptimize expressions of the feature space. The successive effect of the above two aspects contributes a lot to more fine-grained feature spaces, so that recommendation accuracy can be ensured. Two types of experiments are also carried out on three real-world datasets to assess both efficiency and stability of the proposed Deep-PR. Compared with seven typical baselines with respect to four evaluation metrics, obtained results of the overall performance of the Deep-PR are excellent.

Item Type:	Journal article
Publication Title:	IEEE Internet of Things Journal
Creators:	Guo, Z., Yu, K., Kumar, N., Wei, W., Mumtaz, S. and Guizani, M.
Publisher:	Institute of Electrical and Electronics Engineers
Date:	29 August 2022
ISSN:	2327-4662
Identifiers:	Number Type 10.1109/jiot.2022.3202628 DOI 1607026 Other
Rights:	© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Divisions:	Schools > School of Science and Technology
Record created by:	Jonathan Gallacher
Date Added:	13 Oct 2022 09:59
Last Modified:	13 Oct 2022 09:59
URI:	https://irep.ntu.ac.uk/id/eprint/47245

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