AppJoy: personalized mobile application discovery

The explosive growth of the mobile application market has made it a significant challenge for the users to find interesting applications in crowded App Stores. To alleviate this problem, existing industry solutions often use the users’ application download history and possibly their ratings to recommend applications that might interest them, much like Amazon’s book recommendations. However, the user downloading an application is a weak indicator of whether the user likes that application, particularly if the application is free and the user just wants to try it out. Using application ratings, on the other hand, suffers from tedious manual input and potential data sparsity problems. In this paper, we present the AppJoy system that makes personalized application recommendations by analyzing how the user actually uses her installed applications. Based on all participants’ application usage records, AppJoy employs an item-based collaborative filtering algorithm for individualize...

• Type: Journal
• Year: 2011
• Where: MOBISYS
• Authors: Bo Yan, Guanling Chen
• Keywords: Computer Networks, Manual Input, Measurement Performance, MOBISYS 2011, Prediction Algorithm