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1. Deep-Distributed
Recommendation Under Mobile
Networks
Abstract
With the rapid development of edge intelligence in wireless communication
networks, mobile-edge networks (MENs) have been broadly discussed in
academia. Supported by considerable geographical data acquisition ability of
mobile Internet of Things (IoT), the
based social service to users. Therefore, suggesting reasonable points
interest (POIs) to users is essential to improve user experience of MENs. As
the simple user-location data is usually sparse and not informat
literature attempted to extend feature space from two perspectives: 1)
contextual patterns and 2) 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 article, a deep
distributed-learning-based POI recommendation (Deep
proposed for situations of MENs. In particular, hidden feature components
Distributed-Learning-Based POI
Recommendation Under Mobile-Edge
With the rapid development of edge intelligence in wireless communication
edge networks (MENs) have been broadly discussed in
academia. Supported by considerable geographical data acquisition ability of
mobile Internet of Things (IoT), the MENs can also provide spatial locations
based social service to users. Therefore, suggesting reasonable points
interest (POIs) to users is essential to improve user experience of MENs. As
location data is usually sparse and not informat
literature attempted to extend feature space from two perspectives: 1)
contextual patterns and 2) semantic patterns. However, previous approaches
mainly focused on internal features of users, yet ignoring latent external
To address this challenge, in this article, a deep
based POI recommendation (Deep-PR) method is
proposed for situations of MENs. In particular, hidden feature components
Based POI
Edge
With the rapid development of edge intelligence in wireless communication
edge networks (MENs) have been broadly discussed in
academia. Supported by considerable geographical data acquisition ability of
MENs 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 MENs. As
location data is usually sparse and not informative, existing
literature attempted to extend feature space from two perspectives: 1)
contextual patterns and 2) semantic patterns. However, previous approaches
mainly focused on internal features of users, yet ignoring latent external
To address this challenge, in this article, a deep
PR) method is
proposed for situations of MENs. In particular, hidden feature components

2. 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 a recommendation accuracy can be ensured. Two types of
experiments are also carried out on three real-world data sets 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.