Macro-scale mobile app market analysis using customized hierarchical categorization
Xi Liu, Han Hee Song, Mario Baldi, Pang‐Ning Tan · 2016
Thanks to the widespread use of smart devices, recent years have witnessed the proliferation of mobile apps available on online stores such as Apple iTunes and Google Play. As the number of new mobile apps continues to grow at a rapid pace, automatic classification of the apps has become an increasingly important problem to facilitate browsing, searching, and recommending them. This paper presents a framework that automatically labels apps with a richer and more detailed categorization and uses the labeled apps to study the app market. Leveraging a fine-grained, hierarchical ontology as a guide, we developed a framework not only to label the apps with fine-grained categorical information but also to induce a customized class hierarchy optimized for mobile app classification. With the classification accuracy of 93%, large-scale categorization conducted with our framework on 168,000 Google Play apps discovers novel inter-class relationships among categories of Google Play market.