Abstract
With the rise of the mobile Internet, the number of mobile applications (apps) has shown explosive growth, which directly leads to the apps data overload. Currently, the recommender system has become the most effective method to solve the app data overload. However, most of the existing recommended methods for apps ignore the app functional exclusiveness features, which makes it difficult to further improve the app recommendation performance. To solve this problem, we propose a personalized context-aware mobile app recommendation approach, called PCMARA. Specifically, (1) PCMARA explores the relationship between contextual information and function of apps and constructs the app contextual factors for app which represent the function of app. (2) PCMARA leverages the app contextual factor to design a novel app similarity model, which enable to effectively eliminate the adverse effects that ignore the app functional exclusiveness. (3) PCMARA comprehensively considers the contextual information of users and apps to generate a recommendation list for users based on the target users’ current time and location. We applied the PCMARA to a real-world dataset and conducted a large-scale recommendation effect experiment. The experimental results demonstrate the superiority of PMARA over the benchmark methods.
Highlights
With the rise of the mobile Internet and the increase in the number of mobile devices, the number of mobile applications has expanded dramatically
We conjecture that the ignorance of app functional exclusiveness in the process of app recommendation model construction is the reason that the recommendation effect is difficult to further improves
This paper proposes a Personalized Context-aware Mobile App Recommendation Approach, called PCMARA
Summary
With the rise of the mobile Internet and the increase in the number of mobile devices, the number of mobile applications (apps) has expanded dramatically. The huge number of apps makes it difficult for people to find the apps that fit their preferences quickly and causing app data overload problems. Unlike the items in the traditional recommendation domain, such as film [28], news [27], POI [14] and so on, apps have functional exclusiveness features, i.e., if a user has downloaded an app with a certain function to meet his/her specific needs in a certain contextual. The functional exclusiveness features of the app makes the item similarity calculation methods in the traditional recommender system invalid for apps. We explore the app functional exclusiveness features, and in anticipation of improveing the performance of app recommendation in view of incorporating the contextual information
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