Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
A diary study of mobile information needs
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Understanding mobile information needs
Proceedings of the 10th international conference on Human computer interaction with mobile devices and services
A Hybrid Recommender System for Context-aware Recommendations of Mobile Applications
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
Proceedings of the 8th international conference on Mobile systems, applications, and services
LiveLab: measuring wireless networks and smartphone users in the field
ACM SIGMETRICS Performance Evaluation Review
Mobile computing: the next decade
ACM SIGMOBILE Mobile Computing and Communications Review
Identifying diverse usage behaviors of smartphone apps
Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference
Predicting mobile application usage using contextual information
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
End-user recommendations of mobile services: from physical encounters to digital service sharing
Proceeding of the 16th International Academic MindTrek Conference
Climbing the app wall: enabling mobile app discovery through context-aware recommendations
Proceedings of the 21st ACM international conference on Information and knowledge management
App recommendation: a contest between satisfaction and temptation
Proceedings of the sixth ACM international conference on Web search and data mining
Proceedings of the 2013 international conference on Intelligent user interfaces
Proceedings of the 2013 international conference on Intelligent user interfaces
SmartSynth: synthesizing smartphone automation scripts from natural language
Proceeding of the 11th annual international conference on Mobile systems, applications, and services
Categorised ethical guidelines for large scale mobile HCI
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
iLauncher: an intelligent launcher for mobile apps based on individual usage patterns
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Proceedings of the 2013 International Symposium on Wearable Computers
Ranking fraud detection for mobile apps: a holistic view
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems
Multi-objective mobile app recommendation: A system-level collaboration approach
Computers and Electrical Engineering
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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.