Abstract
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.
Highlights
The number of mobile applications has exploded with the development of the mobile internet, making it difficult for users to find apps that truly match their preferences and causing information overload problems
Based on the above considerations, we propose a context-aware mobile app recommendation approach based on user behavior trajectories, called CMARA
This paper proposes a novel context-aware mobile application recommendation approach based on user behavior trajectories, called CMARA
Summary
The number of mobile applications (apps) has exploded with the development of the mobile internet, making it difficult for users to find apps that truly match their preferences and causing information overload problems. 1) CMARA integrates the heterogeneous information of the target user, such as the user’s app, time, and location, into user behavior trajectories to model the user’s app usage. VOLUME 9, 2021 preferences; 2) CMARA constructs a context Voronoi diagram using the user’s contextual point and leverages the context Voronoi diagram to build a user similarity model; 3) CMARA uses target users’ current contextual information to generate an app recommendation list that meets the user’s preferences. CMARA leverages the users’ behavior trajectories to integrate heterogeneous information such as users, apps and user contexts into a combined space to effectively model users’ app preferences. CMARA innovatively uses the users’ context point to design a 3-dimensional context Voronoi diagram and leverages the unique properties of the Voronoi diagram to design a novel and effective user similarity calculation method. We conduct comprehensive experiments by comparing CMARA with four benchmark methods over three real-world data sets
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