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Predicting Application Usage Based on Latent Contextual Information
11 references·1 citing·51 second hop
Predicting Application Usage Based on Latent Contextual Information (2021)Predicting Application Usage Based on Lat…Analyzing movement predictability using human attributes and behavioral patterns (2021)Analyzing movement predictabi…Multi-context embedding based personalized place semantics recognition (2020)Multi-context embedding based…User profiling from their use of smartphone applications: A survey (2019)User profiling from their use…Strategies and limitations in app usage and human mobility (2019)Strategies and limitations in…Modeling Spatio-Temporal App Usage for a Large User Population (2019)Modeling Spatio-Temporal App …The WWW (and an H) of Mobile Application Usage in the City (2018)The WWW (and an H) of Mobile …Smartphone App Usage Prediction Using Points of Interest (2018)Smartphone App Usage Predicti…DeepMove (2018)DeepMoveDeep Learning with Gated Recurrent Unit Networks for Financial Sequence Predictions (2018)Deep Learning with Gated Recu…Describing Patterns and Disruptions in Large Scale Mobile App Usage Data (2017)Describing Patterns and Disru…Intentions to use social media in organizing and taking vacation trips (2010)Intentions to use social medi…Appformer: A novel framework for mobile app usage prediction leveraging progressive multi-modal data fusion and feature extraction (2024)Appformer: A novel framework …Evaluating Structural Equation Models with Unobservable Variables and Measurement Error (1981)Assessing the effects of quality, value, and customer satisfaction on consumer behavioral intentions in service environments (2000)Consumer perceived value: The development of a multiple item scale (2001)The impact of online reviews on hotel booking intentions and perception of trust (2011)Social Media in Tourism and Hospitality: A Literature Review (2013)Social media in marketing: A review and analysis of the existing literature (2017)Models of User Engagement (2012)Churn in the mobile gaming field: Establishing churn definitions and measuring classification similarities (2021)Habits make smartphone use more pervasive (2011)Modeling User Activity Preference by Leveraging User Spatial Temporal Characteristics in LBSNs (2014)Exploring Millions of Footprints in Location Sharing Services (2021)Characterizing Internet Card User Portraits for Efficient Churn Prediction Model Design (2023)Understanding individual human mobility patterns (2008)Limits of Predictability in Human Mobility (2010)Friendship and mobility (2011)Predicting Human Mobility via Variational Attention (2019)Learning to Simulate Human Mobility (2020)A survey on deep learning for human mobility (2023)Local Spatial Autocorrelation Statistics: Distributional Issues and an Application (1995)A Survey Method for Characterizing Daily Life Experience: The Day Reconstruction Method (2004)Jupyter Notebooks – a publishing format for reproducible computational workflows (2016)Tweets on the Go: Gender Differences in Transport Perception and Its Discussion on Social Media (2020)The impact of large-scale events: A difference-in-difference model for a Pokémon go safari zone event in Tainan and its effect on bikeshare systems (2021)Measuring the local complementarity of population, amenities and digital activities to identify and understand urban areas of interest (2022)Efficient Capital Markets: A Review of Theory and Empirical Work (1970)EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK* (1970)Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500 (2016)An improved Stacking framework for stock index prediction by leveraging tree-based ensemble models and deep learning algorithms (2019)Stock Price Forecasting with Deep Learning: A Comparative Study (2020)Stock Prediction Based on Optimized LSTM and GRU Models (2021)The scaling laws of human travel (2006)CellPred (2020)Human Mobility Prediction Using Sparse Trajectory Data (2020)Attentional Markov Model for Human Mobility Prediction (2021)The Big Five Trait taxonomy: History, measurement, and theoretical perspectives. 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(2012)Selective exposure shapes the Facebook news diet (2020)Socio-economic, built environment, and mobility conditions associated with crime: a study of multiple cities (2020)Emerging Digital Inequalities: A Comparative Study of Older Adults’ Smartphone Use (2022)Socio-demographics, activity participation and travel behavior (1999)Using GPS to learn significant locations and predict movement across multiple users (2003)Next place prediction using mobility Markov chains (2012)How do you go where? (2022)Income estimation based on human mobility patterns and machine learning models (2023)Predicting mobile users' next location using the semantically enriched geo-embedding model and the multilayer attention mechanism (2023)
this work references cited by second hop

References

  • 2018DeepMove714 cited
  • 2010Intentions to use social media in organizing and taking vacation trips410 cited
  • 2018Deep Learning with Gated Recurrent Unit Networks for Financial Sequence Predictions304 cited
  • 2018Smartphone App Usage Prediction Using Points of Interest101 cited
  • 2019User profiling from their use of smartphone applications: A survey60 cited
  • 2019Strategies and limitations in app usage and human mobility36 cited
  • 2019Modeling Spatio-Temporal App Usage for a Large User Population33 cited
  • 2021Analyzing movement predictability using human attributes and behavioral patterns30 cited
  • 2018The WWW (and an H) of Mobile Application Usage in the City21 cited
  • 2017Describing Patterns and Disruptions in Large Scale Mobile App Usage Data20 cited
  • 2020Multi-context embedding based personalized place semantics recognition9 cited

Cited by

  • 2024Appformer: A novel framework for mobile app usage prediction leveraging progressive multi-modal data fusion and feature extraction6 cited

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