Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Modern Information Retrieval
Automatic image annotation and retrieval using cross-media relevance models
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
The Journal of Machine Learning Research
The author-topic model for authors and documents
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
ContextPhone: A Prototyping Platform for Context-Aware Mobile Applications
IEEE Pervasive Computing
Reality mining: sensing complex social systems
Personal and Ubiquitous Computing
Mobile data service fuels the desire for uniqueness
Communications of the ACM - Privacy and security in highly dynamic systems
What did you do today?: discovering daily routines from large-scale mobile data
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Analysis of users and non-users of smartphone applications
Telematics and Informatics
Design and analysis of the KDD cup 2009: fast scoring on a large orange customer database
ACM SIGKDD Explorations Newsletter
Smartphone usage in the wild: a large-scale analysis of applications and context
ICMI '11 Proceedings of the 13th international conference on multimodal interfaces
Predicting mobile application usage using contextual information
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Mining large-scale smartphone data for personality studies
Personal and Ubiquitous Computing
Predicting personality using novel mobile phone-based metrics
SBP'13 Proceedings of the 6th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
A study on icon arrangement by smartphone users
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
On mining mobile apps usage behavior for predicting apps usage in smartphones
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems
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Mobile phones are becoming more and more widely used nowadays, and people do not use the phone only for communication: there is a wide variety of phone applications allowing users to select those that fit their needs. Aggregated over time, application usage patterns exhibit not only what people are consistently interested in but also the way in which they use their phones, and can help improving phone design and personalized services. This work aims at mining automatically usage patterns from apps data recorded continuously with smartphones. A new probabilistic framework for mining usage patterns is proposed. Our methodology involves the design of a bag-of-apps model that robustly represents level of phone usage over specific times of the day, and the use of a probabilistic topic model that jointly discovers patterns of usage over multiple applications and describes users as mixtures of such patterns. Our framework is evaluated using 230 000+ hours of real-life app phone log data, demonstrates that relevant patterns of usage can be extracted, and is objectively validated on a user retrieval task with competitive performance.