Understanding the intent behind mobile information needs
Proceedings of the 14th international conference on Intelligent user interfaces
Collaborative Filtering for Implicit Feedback Datasets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
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
Large-scale behavioral targeting
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Mind the gaps: weighting the unknown in large-scale one-class collaborative filtering
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Performance of recommender algorithms on top-n recommendation tasks
Proceedings of the fourth ACM conference on Recommender systems
Proceedings of the fourth ACM conference on Recommender systems
One-Class Matrix Completion with Low-Density Factorizations
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
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
Proceedings of the 2013 international conference on Intelligent user interfaces
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The explosive growth of the mobile application (app) market has made it difficult for users to find the most interesting and relevant apps from the hundreds of thousands that exist today. Context is key in the mobile space and so too are proactive services that ease user input and facilitate effective interaction. We believe that to enable truly novel mobile app recommendation and discovery, we need to support real context-aware recommendation that utilizes the diverse range of implicit mobile data available in a fast and scalable manner. In this paper we introduce the Djinn model, a novel context-aware collaborative filtering algorithm for implicit feedback data that is based on tensor factorization. We evaluate our approach using a dataset from an Android mobile app recommendation service called appazaar. Our results show that our approach compares favorably with state-of-the-art collaborative filtering methods.