Y. Liu et al., "Interaction-Enhanced and Time-Aware Graph Convolutional Network for Successive Point-of-Interest Recommendation in Traveling Enterprises," in IEEE Transactions on Industrial Informatics, vol. 19, no. 1, pp. 635-643, Jan. 2023, doi: 10.1109/TII.2022.3200067
Interaction-enhanced and time-aware graph convolutional network for successive point-of-interest recommendation in traveling enterprises
|Author:||Liu, Yuwen1; Wu, Huiping2; Rezaee, Khosro3;|
1College of Computer Science and Technology, China University of Petroleum, Qingdao, China
2Shandong Provincial University Laboratory for Protected Horticulture, Weifang University of Science and Technology, Weifang, China
3Meybod University, Meybod, Iran
4Department of Computer Engineering, Persian Gulf University, Bushehr, Iran
5Al-Nahrain Nanorenewable Energy Research Center, Al-Nahrain University, Baghdad, Iraq
6M3S Empirical Software Engineering Research Unit, University of Oulu, Oulu, Finland
7Department of Computer Science and Engineering, Indian Institute of Technology (ISM), Dhanbad, India
|Online Access:||PDF Full Text (PDF, 2.5 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2023030229228
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2023-03-02
Extensive user check-in data incorporating user preferences for location is collected through Internet of Things (IoT) devices, including cell phones and other sensing devices in location-based social network. It can help traveling enterprises intelligently predict users’ interests and preferences, provide them with scientific tourism paths, and increase the enterprises income. Thus, successive point-of-interest (POI) recommendation has become a hot research topic in augmented Intelligence of Things (AIoT). Presently, various methods have been applied to successive POI recommendations. Among them, the recurrent neural network-based approaches are committed to mining the sequence relationship between POIs, but ignore the high-order relationship between users and POIs. The graph neural network-based methods can capture the high-order connectivity, but it does not take the dynamic timeliness of POIs into account. Therefore, we propose an I nteraction-enhanced and T ime-aware G raph C onvolution N etwork (ITGCN) for successive POI recommendation. Specifically, we design an improved graph convolution network for learning the dynamic representation of users and POIs. We also designed a self-attention aggregator to embed high-order connectivity into the node representation selectively. The enterprise management systems can predict the preferences of users, which is helpful for future planning and development. Finally, experimental results prove that ITGCN brings better results compared to the existing methods.
IEEE transactions on industrial informatics
|Pages:||635 - 643|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
113 Computer and information sciences
This research is supported by the National Key Research and Development Program of China (Grant Number: 2020YFB1707600); 10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 61872219 and 62177014).
© 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.