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Appformer: A novel framework for mobile app usage prediction leveraging progressive multi-modal data fusion and feature extraction
7 references·0 citing·35 second hop
Appformer: A novel framework for mobile app usage prediction leveraging progressive multi-modal data fusion and feature extraction (2024)Appformer: A novel framework for mobile a…Predicting determinants influencing user satisfaction with mental health app: An explainable machine learning approach based on unstructured data (2024)Predicting determinants influ…Predicting Application Usage Based on Latent Contextual Information (2021)Predicting Application Usage …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…On the elicitation of criteria weights in PROMETHEE-based ranking methods for a mobile application (2018)On the elicitation of criteri…Habits make smartphone use more pervasive (2011)Modeling User Activity Preference by Leveraging User Spatial Temporal Characteristics in LBSNs (2014)User profiling from their use of smartphone applications: A survey (2019)Exploring Millions of Footprints in Location Sharing Services (2021)Characterizing Internet Card User Portraits for Efficient Churn Prediction Model Design (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)Assessing a set of additive utility functions for multicriteria decision-making, the UTA method (1982)Note—A Preference Ranking Organisation Method (1985)Decisions with Multiple Objectives: Preferences and Value Tradeoffs. (1994)A Hybrid Decision Making Framework for Personnel Selection Using BWM, MABAC and PROMETHEE (2019)Decision making application in collaborative emergency response: A new PROMETHEE preference function (2019)Principal Component Analysis in MCDM: An exercise in pilot selection (2021)The scaling laws of human travel (2006)Understanding individual human mobility patterns (2008)Friendship and mobility (2011)CellPred (2020)Human Mobility Prediction Using Sparse Trajectory Data (2020)Attentional Markov Model for Human Mobility Prediction (2021)Systems Competition and Network Effects (1994)Limits of Predictability in Human Mobility (2010)Social network changes and life events across the life span: A meta-analysis. (2012)Selective exposure shapes the Facebook news diet (2020)Putting human behavior predictability in context (2021)Emerging Digital Inequalities: A Comparative Study of Older Adults’ Smartphone Use (2022)Intentions to use social media in organizing and taking vacation trips (2010)Deep Learning with Gated Recurrent Unit Networks for Financial Sequence Predictions (2018)DeepMove (2018)Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology (1989)Positive psychology: An introduction. (2000)Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. (2000)Insights from User Reviews to Improve Suicide Prevention Apps: A Machine Learning and Thematic Analysis-Based Approach (2025)
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References

  • 2018Smartphone App Usage Prediction Using Points of Interest101 cited
  • 2018On the elicitation of criteria weights in PROMETHEE-based ranking methods for a mobile application59 cited
  • 2019Strategies and limitations in app usage and human mobility36 cited
  • 2019Modeling Spatio-Temporal App Usage for a Large User Population33 cited
  • 2024Predicting determinants influencing user satisfaction with mental health app: An explainable machine learning approach based on unstructured data27 cited
  • 2018The WWW (and an H) of Mobile Application Usage in the City21 cited
  • 2021Predicting Application Usage Based on Latent Contextual Information8 cited

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