Bo Yan
ABSTRACT
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 individualized recommendations. We discuss AppJoy's design and implementation, and the evaluation shows that it consumes little resource on the off-the-shelf Google Android phones. AppJoy has been available in the Android Market and used by more than 4600 users. The AppJoy's prediction algorithm provided reasonably accurate usage estimate of the recommended applications after they were installed. We also found AppJoy to be effective as the users interacted with recommended applications longer than other applications.
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Index Terms
- AppJoy: personalized mobile application discovery
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