A Novel Context-Aware Mobile Application Recommendation Approach Based on Users Behavior Trajectories

被引:6
作者
Zhu, Ke [1 ,2 ]
Xiao, Yingyuan [1 ,2 ]
Zheng, Wenguang [1 ,2 ]
Jiao, Xu [3 ]
Hsu, Ching-Hsien [4 ]
机构
[1] Tianjin Univ Technol, Tianjin Key Lab Intelligence Comp & Novel Softwar, Tianjin 300384, Peoples R China
[2] Minist Educ, Engn Res Ctr Learning Based Intelligent Syst, Tianjin 300384, Peoples R China
[3] Tianjin Foreign Studies Univ, Coll Gen Educ, Tianjin 300204, Peoples R China
[4] Asia Univ, Dept Comp Sci & Informat Engn, Taichung 41354, Taiwan
关键词
Collaborative filtering; app recommendation; voronoi diagram; behavior trajectories;
D O I
10.1109/ACCESS.2020.3046654
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:1362 / 1375
页数:14
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