Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
User needs for location-aware mobile services
Personal and Ubiquitous Computing
Being accurate is not enough: how accuracy metrics have hurt recommender systems
CHI '06 Extended Abstracts on Human Factors in Computing Systems
Contextual patterns in mobile service usage
Personal and Ubiquitous Computing
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
A case study on the effectiveness of recommendations in the mobile internet
Proceedings of the third ACM conference on Recommender systems
Context-based splitting of item ratings in collaborative filtering
Proceedings of the third ACM conference on Recommender systems
What happens after an ad click?: quantifying the impact of landing pages in web advertising
Proceedings of the 18th ACM conference on Information and knowledge management
Ranking for the conversion funnel
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
AppAware: which mobile applications are hot?
Proceedings of the 12th international conference on Human computer interaction with mobile devices and services
Performance of recommender algorithms on top-n recommendation tasks
Proceedings of the fourth ACM conference on Recommender systems
Utilizing implicit feedback and context to recommend mobile applications from first use
Proceedings of the 2011 Workshop on Context-awareness in Retrieval and Recommendation
AppJoy: personalized mobile application discovery
MobiSys '11 Proceedings of the 9th international conference on Mobile systems, applications, and services
Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services
A user-centric evaluation framework for recommender systems
Proceedings of the fifth ACM conference on Recommender systems
Post-click conversion modeling and analysis for non-guaranteed delivery display advertising
Proceedings of the fifth ACM international conference on Web search and data mining
Recommender systems: from algorithms to user experience
User Modeling and User-Adapted Interaction
CHI '12 Extended Abstracts on Human Factors in Computing Systems
GetJar mobile application recommendations with very sparse datasets
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Explaining the user experience of recommender systems
User Modeling and User-Adapted Interaction
TFMAP: optimizing MAP for top-n context-aware recommendation
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Understanding and prediction of mobile application usage for smart phones
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Climbing the app wall: enabling mobile app discovery through context-aware recommendations
Proceedings of the 21st ACM international conference on Information and knowledge management
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
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Mobile phones have evolved from communication to multi-purpose devices that assist people with applications in various contexts and tasks. The size of the mobile ecosystem is steadily growing and new applications become available every day. This increasing number of applications makes it difficult for end-users to find good applications. Recommender systems suggesting mobile applications are being built to help people to find valuable applications. Since the nature of mobile applications differs from classical items to be recommended (e.g. books, movies, other goods), not only can new approaches for recommendation be developed, but also new paradigms for evaluating performance of recommender systems are advisable. During the lifecycle of mobile applications, different events can be observed that provide insights into users' engagement with particular applications. This gives rise to new approaches for evaluation of recommender systems. In this paper, we present AppFunnel: a framework that allows for usage-centric evaluation considering different stages of application engagement. We present a case study and discuss capabilities for evaluating recommender engines by applying metrics to the AppFunnel.