SimApp

With the popularity of smart phones and mobile devices, the number of mobile applications (a.k.a. \"apps\") has been growing rapidly. Detecting semantically similar apps from a large pool of apps is a basic and important problem, as it is beneficial for various applications, such as app recommendation, app search, etc. However, there is no systematic and comprehensive work so far that focuses on addressing this problem. In order to fill this gap, in this paper, we explore multi-modal heterogeneous data in app markets (e.g., description text, images, user reviews, etc.), and present \"SimApp\" -- a novel framework for detecting similar apps using machine learning. Specifically, it consists of two stages: (i) a variety of kernel functions are constructed to measure app similarity for each modality of data; and (ii) an online kernel learning algorithm is proposed to learn the optimal combination of similarity functions of multiple modalities. We conduct an extensive set of experiments on a real-world dataset crawled from Google Play to evaluate SimApp, from which the encouraging results demonstrate that SimApp is effective and promising.