A Novel Context-Aware Mobile Application Recommendation Approach Based on Users Behavior Trajectories
With the rapid development of mobile internet technology, mobile applications (apps) have been rapidly popularized. To facilitate users' choice of apps, app recommendation is becoming a research hotspot in academia and industry. Although traditional app recommendation approaches have achieved c...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9303368/ |
| _version_ | 1818448979130580992 |
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| author | Ke Zhu Yingyuan Xiao Wenguang Zheng Xu Jiao Ching-Hsien Hsu |
| author_facet | Ke Zhu Yingyuan Xiao Wenguang Zheng Xu Jiao Ching-Hsien Hsu |
| author_sort | Ke Zhu |
| collection | DOAJ |
| description | With the rapid development of mobile internet technology, mobile applications (apps) have been rapidly popularized. To facilitate users' choice of apps, app recommendation is becoming a research hotspot in academia and industry. Although traditional app recommendation approaches have achieved certain results, these methods only mechanically consider the user's current context information, ignoring the impact of the user's previous related context on the user's current selection of apps. We believe this has hindered the further improvement of the recommendation effect. Based on this fact, this paper proposes a novel context-aware mobile application recommendation approach based on user behavior trajectories. We named this approach CMARA, which is the initials acronym of the proposed approach. Specifically, 1) CMARA integrates the heterogeneous information of the target users such as the user's app, time, and location, into users behavior trajectories to model the users' app usage preferences; 2) CMARA constructs the context Voronoi diagram using the users' contextual point and leverages the context Voronoi diagram to build a novel user similarity model; 3) CMARA uses the target user's current contextual information to generate an app recommendation list that meets the user's preferences. Through experiments on large-scale real-world data, we verified the effectiveness of CMARA. |
| first_indexed | 2024-12-14T20:28:07Z |
| format | Article |
| id | doaj.art-eda81e08d2654fb2a6858e6c58d1c02b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T20:28:07Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-eda81e08d2654fb2a6858e6c58d1c02b2022-12-21T22:48:35ZengIEEEIEEE Access2169-35362021-01-0191362137510.1109/ACCESS.2020.30466549303368A Novel Context-Aware Mobile Application Recommendation Approach Based on Users Behavior TrajectoriesKe Zhu0https://orcid.org/0000-0003-3178-3493Yingyuan Xiao1https://orcid.org/0000-0002-5711-8638Wenguang Zheng2Xu Jiao3https://orcid.org/0000-0001-5658-5202Ching-Hsien Hsu4https://orcid.org/0000-0002-2440-2771Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin, ChinaTianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin, ChinaTianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin, ChinaCollege of General Education, Tianjin Foreign Studies University, Tianjin, ChinaDepartment of Computer Science and Information Engineering, Asia University, Taichung, TaiwanWith the rapid development of mobile internet technology, mobile applications (apps) have been rapidly popularized. To facilitate users' choice of apps, app recommendation is becoming a research hotspot in academia and industry. Although traditional app recommendation approaches have achieved certain results, these methods only mechanically consider the user's current context information, ignoring the impact of the user's previous related context on the user's current selection of apps. We believe this has hindered the further improvement of the recommendation effect. Based on this fact, this paper proposes a novel context-aware mobile application recommendation approach based on user behavior trajectories. We named this approach CMARA, which is the initials acronym of the proposed approach. Specifically, 1) CMARA integrates the heterogeneous information of the target users such as the user's app, time, and location, into users behavior trajectories to model the users' app usage preferences; 2) CMARA constructs the context Voronoi diagram using the users' contextual point and leverages the context Voronoi diagram to build a novel user similarity model; 3) CMARA uses the target user's current contextual information to generate an app recommendation list that meets the user's preferences. Through experiments on large-scale real-world data, we verified the effectiveness of CMARA.https://ieeexplore.ieee.org/document/9303368/Collaborative filteringapp~recommendationvoronoi diagrambehavior trajectories |
| spellingShingle | Ke Zhu Yingyuan Xiao Wenguang Zheng Xu Jiao Ching-Hsien Hsu A Novel Context-Aware Mobile Application Recommendation Approach Based on Users Behavior Trajectories IEEE Access Collaborative filtering app~recommendation voronoi diagram behavior trajectories |
| title | A Novel Context-Aware Mobile Application Recommendation Approach Based on Users Behavior Trajectories |
| title_full | A Novel Context-Aware Mobile Application Recommendation Approach Based on Users Behavior Trajectories |
| title_fullStr | A Novel Context-Aware Mobile Application Recommendation Approach Based on Users Behavior Trajectories |
| title_full_unstemmed | A Novel Context-Aware Mobile Application Recommendation Approach Based on Users Behavior Trajectories |
| title_short | A Novel Context-Aware Mobile Application Recommendation Approach Based on Users Behavior Trajectories |
| title_sort | novel context aware mobile application recommendation approach based on users behavior trajectories |
| topic | Collaborative filtering app~recommendation voronoi diagram behavior trajectories |
| url | https://ieeexplore.ieee.org/document/9303368/ |
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