On the public citation graph

Understanding and prediction of mobile application usage for smart phones

0 signed · 276 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.

Claim with ORCID

Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.

See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-1-4899-7687-1_100245openalex-snapshot

Learning Bayesian Networks

inferredcites →https://doi.org/10.1007/978-3-642-03655-2_86openalex-snapshot

SmartActions: Context-Aware Mobile Phone Shortcuts

inferredcites →https://doi.org/10.1007/s00779-003-0240-0openalex-snapshot

Using GPS to learn significant locations and predict movement across multiple users

inferredcites →https://doi.org/10.1007/s00779-005-0046-3openalex-snapshot

Reality mining: sensing complex social systems

inferredcites →https://doi.org/10.1007/s00779-008-0197-0openalex-snapshot

Contextual patterns in mobile service usage

inferredcites →https://doi.org/10.1016/b978-012370477-1/50021-9openalex-snapshot

Learning Bayesian Networks

inferredcites →https://doi.org/10.1016/j.eswa.2009.03.002openalex-snapshot

Landmark detection from mobile life log using a modular Bayesian network model

inferredcites →https://doi.org/10.1109/inss.2008.4610872openalex-snapshot

Proposition of the context-aware interface for cellular phone operations

inferredcites →https://doi.org/10.1109/mcom.2011.5783977openalex-snapshot

Conference calendar

inferredcites →https://doi.org/10.1109/mcom.2011.5783996openalex-snapshot

An adaptive user interface based on spatiotemporal structure learning

inferredcites →https://doi.org/10.1109/mprv.2005.39openalex-snapshot

Swiss Army Knife or Wallet?

inferredcites →https://doi.org/10.1109/saint.2009.12openalex-snapshot

Operation Prediction for Context-Aware User Interfaces of Mobile Phones

inferredcites →https://doi.org/10.1145/1111449.1111526openalex-snapshot

Inducing shortcuts on a mobile phone interface

inferredcites →https://doi.org/10.1145/1247660.1247670openalex-snapshot

MyExperience

inferredcites →https://doi.org/10.1145/1613858.1613921openalex-snapshot

Automatic mobile menu customization based on user operation history

inferredcites →https://doi.org/10.1145/174630.174632openalex-snapshot

Split menus

inferredcites →https://doi.org/10.1145/2037373.2037383openalex-snapshot

Falling asleep with Angry Birds, Facebook and Kindle

inferredcites →https://doi.org/10.1214/aoms/1177729694openalex-snapshot

On Information and Sufficiency

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.2669817openalex-extracted

Creating Customer Engagement Via Mobile Apps: How App Usage Drives Purchase Behavior

inferred← citeshttps://doi.org/10.3217/jucs-020-14-1964openalex-extracted

Context Classification Framework for Handset-based End User Studies

inferred← citeshttps://doi.org/10.1002/cpe.5709openalex-snapshot

Improving the impact of power efficiency in mobile cloud applications using cloudlet model

inferred← citeshttps://doi.org/10.1007/978-3-030-15032-7_50openalex-snapshot

Mobile App and Malware Classifications by Mobile Usage with Time Dynamics

inferred← citeshttps://doi.org/10.1007/978-3-030-52237-7_8openalex-snapshot

Predicting Gaps in Usage in a Phone-Based Literacy Intervention System

inferred← citeshttps://doi.org/10.1007/978-3-030-59016-1_56openalex-snapshot

SEM: APP Usage Prediction with Session-Based Embedding

inferred← citeshttps://doi.org/10.1007/978-3-030-65923-3_9openalex-snapshot

Millennials and On-line Shopping: The Case of Smartphones

inferred← citeshttps://doi.org/10.1007/978-3-030-88530-4_3openalex-snapshot

A Literature Review on Context-Aware Machine Learning and Mobile Data Analytics

inferred← citeshttps://doi.org/10.1007/978-3-031-35385-7_15openalex-snapshot

Effects of Animation Types on Visual Search Behavior: A Study on Mobile Icon Search

inferred← citeshttps://doi.org/10.1007/978-3-031-43427-3_7openalex-snapshot

Sequence-Graph Fusion Neural Network for User Mobile App Behavior Prediction

inferred← citeshttps://doi.org/10.1007/978-3-319-05452-0_11openalex-snapshot

Reconciling Cloud and Mobile Computing Using Activity-Based Predictive Caching

inferred← citeshttps://doi.org/10.1007/978-3-319-05452-0_6openalex-snapshot

Smartphone Interactions Change for Different Intimacy Contexts

inferred← citeshttps://doi.org/10.1007/978-3-319-05579-4_24openalex-snapshot

mFingerprint: Privacy-Preserving User Modeling with Multimodal Mobile Device Footprints

inferred← citeshttps://doi.org/10.1007/978-3-319-07650-8_8openalex-snapshot

Implementation of Agent-Based Games Recommendation System on Mobile Platforms

inferred← citeshttps://doi.org/10.1007/978-3-319-19656-5_49openalex-snapshot

To Run or Not to Run: Predicting Resource Usage Pattern in a Smartphone

inferred← citeshttps://doi.org/10.1007/978-3-319-23437-3_56openalex-snapshot

Characteristic of User Generated Load in Mobile Gaming Environment

inferred← citeshttps://doi.org/10.1007/978-3-319-26979-5_1openalex-snapshot

A Context-Aware Usage Prediction Approach for Smartphone Applications

inferred← citeshttps://doi.org/10.1007/978-3-319-42996-0_7openalex-snapshot

Predicting Phone Usage Behaviors with Sensory Data Using a Hierarchical Generative Model

inferred← citeshttps://doi.org/10.1007/978-3-319-48740-3_25openalex-snapshot

Predicting Replacement of Smartphones with Mobile App Usage

inferred← citeshttps://doi.org/10.1007/978-3-319-70139-4_52openalex-snapshot

App Uninstalls Prediction: A Machine Learning and Time Series Mining Approach

inferred← citeshttps://doi.org/10.1007/978-3-319-78130-3_50openalex-snapshot

Positive and Unlabeled Learning for Mobile App User and Server Interaction Prediction

inferred← citeshttps://doi.org/10.1007/978-3-319-94268-1_44openalex-snapshot

Predicting Smartphone App Usage with Recurrent Neural Networks

inferred← citeshttps://doi.org/10.1007/978-3-662-44848-9_43openalex-snapshot

Conditional Log-linear Models for Mobile Application Usage Prediction

inferred← citeshttps://doi.org/10.1007/978-981-10-3941-6_8openalex-snapshot

Mobile OS and Application Protocols

inferred← citeshttps://doi.org/10.1007/s00521-023-09400-4openalex-snapshot

Customer satisfaction analysis with Saudi Arabia mobile banking apps: a hybrid approach using text mining and predictive learning techniques

inferred← citeshttps://doi.org/10.1007/s00779-018-01190-0openalex-snapshot

Energy-efficient prediction of smartphone unlocking

inferred← citeshttps://doi.org/10.1007/s10639-018-9840-1openalex-snapshot

Analysis of the essential factors affecting of intention to use of mobile learning applications: A comparison between universities adopters and non-adopters

inferred← citeshttps://doi.org/10.1007/s10707-018-0322-9openalex-snapshot

Mining mobile application usage pattern for demand prediction by considering spatial and temporal relations

inferred← citeshttps://doi.org/10.1007/s11257-024-09395-0openalex-snapshot

Federated privacy-preserving collaborative filtering for on-device next app prediction

inferred← citeshttps://doi.org/10.1007/s40558-018-0123-xopenalex-snapshot

Users’ acceptance of innovative mobile hotel booking trends: UK vs. PRC

inferred← citeshttps://doi.org/10.1007/s42486-020-00041-3openalex-snapshot

Demographics of mobile app usage: long-term analysis of mobile app usage

inferred← citeshttps://doi.org/10.1007/s42486-020-00045-zopenalex-snapshot

Predicting and Recommending the next Smartphone Apps based on Recurrent Neural Network

inferred← citeshttps://doi.org/10.1016/b978-0-12-409548-9.09673-1openalex-snapshot

Sensing and Modeling Human Behavior Using Social Media and Mobile Data

inferred← citeshttps://doi.org/10.1016/b978-0-12-416691-2.00013-1openalex-snapshot

Problem 4

inferred← citeshttps://doi.org/10.1016/j.comcom.2022.06.005openalex-snapshot

Predicting Application Usage Based on Latent Contextual Information

inferred← citeshttps://doi.org/10.1016/j.comnet.2020.107737openalex-snapshot

Clustering and predicting the data usage patterns of geographically diverse mobile users

inferred← citeshttps://doi.org/10.1016/j.eswa.2024.125903openalex-snapshot

Appformer: A novel framework for mobile app usage prediction leveraging progressive multi-modal data fusion and feature extraction

inferred← citeshttps://doi.org/10.1016/j.ijhcs.2020.102497openalex-snapshot

Predicting mid-air gestural interaction with public displays based on audience behaviour

inferred← citeshttps://doi.org/10.1016/j.ijresmar.2019.01.002openalex-snapshot

Measuring and forecasting mobile game app engagement

inferred← citeshttps://doi.org/10.1016/j.ins.2017.11.040openalex-snapshot

A multi-granularity perspective for spatial profiling of mobile apps

inferred← citeshttps://doi.org/10.1016/j.iot.2019.01.007openalex-snapshot

A machine learning based robust prediction model for real-life mobile phone data

inferred← citeshttps://doi.org/10.1016/j.jisa.2017.10.002openalex-snapshot

A survey on behavioral biometric authentication on smartphones

inferred← citeshttps://doi.org/10.1016/j.physa.2018.09.135openalex-snapshot

Using combined network information to predict mobile application usage

inferred← citeshttps://doi.org/10.1016/j.pmcj.2017.01.007openalex-snapshot

Mining smartphone data for app usage prediction and recommendations: A survey

inferred← citeshttps://doi.org/10.1016/j.pmcj.2019.05.006openalex-snapshot

App usage prediction for dual display device via two-phase sequence modeling

inferred← citeshttps://doi.org/10.1016/j.pmcj.2019.101052openalex-snapshot

User profiling from their use of smartphone applications: A survey

inferred← citeshttps://doi.org/10.1016/j.pmcj.2022.101704openalex-snapshot

App usage on-the-move: Context- and commute-aware next app prediction

inferred← citeshttps://doi.org/10.1016/j.procs.2013.06.060openalex-snapshot

AppRush: Using Dynamic Shortcuts to Facilitate Application Launching on Mobile Devices

inferred← citeshttps://doi.org/10.1016/j.procs.2017.11.460openalex-snapshot

Decline in Mobile Application Life Cycle

inferred← citeshttps://doi.org/10.1016/j.suscom.2018.08.009openalex-snapshot

HiNextApp: A context-aware and adaptive framework for app prediction in mobile systems

inferred← citeshttps://doi.org/10.1038/s41598-019-47493-xopenalex-snapshot

Strategies and limitations in app usage and human mobility

inferred← citeshttps://doi.org/10.1038/s41598-025-05260-1openalex-snapshot

Next app prediction based on graph neural networks and self-attention enhancement

inferred← citeshttps://doi.org/10.1063/1.5055451openalex-snapshot

The evaluation of TBS mobile application: Users experience

inferred← citeshttps://doi.org/10.1080/0144929x.2016.1186735openalex-snapshot

Predicting user preferences of environment design: a perceptual mechanism of user interface customisation

inferred← citeshttps://doi.org/10.1080/0144929x.2020.1714733openalex-snapshot

MapRecorder: analysing real-world usage of mobile map applications

inferred← citeshttps://doi.org/10.1080/0144929x.2021.1871954openalex-snapshot

Sequential UI behaviour prediction system based on long short-term memory networks

inferred← citeshttps://doi.org/10.1080/0965254x.2016.1195857openalex-snapshot

An examination of mobile app usage and the user’s life satisfaction

inferred← citeshttps://doi.org/10.1098/rsos.190018openalex-snapshot

Evidence to support common application switching behaviour on smartphones

inferred← citeshttps://doi.org/10.1108/ajim-08-2021-0220openalex-snapshot

Characterizing and predicting the cross-app behavior in mobile search

inferred← citeshttps://doi.org/10.1108/imds-04-2016-0141openalex-snapshot

Analysing customer behaviour in mobile app usage

inferred← citeshttps://doi.org/10.1108/jamr-06-2020-0128openalex-snapshot

Exploring engagement among mobile app developers – Insights from mining big data in user generated content

inferred← citeshttps://doi.org/10.1109/access.2016.2518179openalex-snapshot

Offload Decision Models and the Price of Anarchy in Mobile Cloud Application Ecosystems

inferred← citeshttps://doi.org/10.1109/access.2018.2852008openalex-snapshot

Positive and Unlabeled Learning for User Behavior Analysis Based on Mobile Internet Traffic Data

inferred← citeshttps://doi.org/10.1109/access.2019.2918325openalex-snapshot

Enhancing Mobile App User Understanding and Marketing With Heterogeneous Crowdsourced Data: A Review

inferred← citeshttps://doi.org/10.1109/access.2019.2942362openalex-snapshot

$ezswap$ : Enhanced Compressed Swap Scheme for Mobile Devices

inferred← citeshttps://doi.org/10.1109/access.2022.3171579openalex-snapshot

Prediction of Application Usage on Smartphones via Deep Learning

inferred← citeshttps://doi.org/10.1109/access.2024.3372397openalex-snapshot

Enhancing App Usage Prediction Accuracy With GCN-Transformer Model and Meta-Path Context

inferred← citeshttps://doi.org/10.1109/bdcloud-socialcom-sustaincom.2016.49openalex-snapshot

Exploring Smartphone Application Usage Logs with Declared Sociological Information

inferred← citeshttps://doi.org/10.1109/bigcomp57234.2023.00051openalex-snapshot

Predicting Next Application Most Likely Used with Word Embedding and Time-Series Data Encoding

inferred← citeshttps://doi.org/10.1109/comst.2022.3163176openalex-snapshot

Smartphone App Usage Analysis: Datasets, Methods, and Applications

inferred← citeshttps://doi.org/10.1109/grc.2014.6982832openalex-snapshot

Mining mobile application sequential patterns for usage prediction

inferred← citeshttps://doi.org/10.1109/healthcom.2016.7749476openalex-snapshot

Still in flow — long-term usage of an activity motivating app for seniors

inferred← citeshttps://doi.org/10.1109/hpca61900.2025.00118openalex-snapshot

Ariadne: A Hotness-Aware and Size-Adaptive Compressed Swap Technique for Fast Application Relaunch and Reduced CPU Usage on Mobile Devices

inferred← citeshttps://doi.org/10.1109/hpcc/smartcity/dss.2018.00086openalex-snapshot

Developing Offloading-Enabled Application Development Frameworks for Android Mobile Devices

inferred← citeshttps://doi.org/10.1109/icac.2017.14openalex-snapshot

CoSmart: Coordinating Smartphone with Desktop Computer for Joint Energy Savings

inferred← citeshttps://doi.org/10.1109/icbda47563.2019.8987044openalex-snapshot

Comparing the Accuracy of Hierarchical Agglomerative and K-means Clustering on Mobile Augmented Reality Usability Metrics

inferred← citeshttps://doi.org/10.1109/icce.2014.6776032openalex-snapshot

Disappearing icons: Informative effect through changing color attributes of app icons

inferred← citeshttps://doi.org/10.1109/iccnc.2015.7069406openalex-snapshot

Content wanted: A different shade of D2D communications

inferred← citeshttps://doi.org/10.1109/iccsre.2019.8807446openalex-snapshot

Bazaar Mind: A Virtual Shopping Assistant

inferred← citeshttps://doi.org/10.1109/icde.2019.00120openalex-snapshot

AppUsage2Vec: Modeling Smartphone App Usage for Prediction

inferred← citeshttps://doi.org/10.1109/icdm.2013.130openalex-snapshot

On the Feature Discovery for App Usage Prediction in Smartphones

inferred← citeshttps://doi.org/10.1109/icgciot.2015.7380520openalex-snapshot

Smart web miner - extending web browser with mining framework based on user behavior & web-of-thing patterns for web personalization

inferred← citeshttps://doi.org/10.1109/icmla.2019.00054openalex-snapshot

Learning Mobile Application Usage - A Deep Learning Approach

inferred← citeshttps://doi.org/10.1109/icnp.2017.8117544openalex-snapshot

iMUTE: Energy-optimal update policy for perishable mobile contents

inferred← citeshttps://doi.org/10.1109/icter.2015.7377693openalex-snapshot

Towards optimising Wi-Fi energy consumption in mobile phones: A data driven approach

inferred← citeshttps://doi.org/10.1109/icws.2018.00038openalex-snapshot

A Sequential Recommendation for Mobile Apps: What Will User Click Next App?

inferred← citeshttps://doi.org/10.1109/igcc.2017.8323571openalex-snapshot

Selective checkpointing for minimizing recovery energy and efforts of smartphone apps

inferred← citeshttps://doi.org/10.1109/ijcnn.2017.7965952openalex-snapshot

Sequence-to-sequence prediction of personal computer software by recurrent neural network

inferred← citeshttps://doi.org/10.1109/isce.2014.6884301openalex-snapshot

Changing the color attributes of icons to inform of the application status

inferred← citeshttps://doi.org/10.1109/isie51582.2022.9831540openalex-snapshot

Machine Learning Methods for Smartphone Application Prediction

inferred← citeshttps://doi.org/10.1109/islped.2017.8009195openalex-snapshot

Online tuning of Dynamic Power Management for efficient execution of interactive workloads

inferred← citeshttps://doi.org/10.1109/iwqos65803.2025.11265768openalex-snapshot

Mapp: Predictive UI View Pre-Caching for Improving the Responsiveness of Mobile Apps

inferred← citeshttps://doi.org/10.1109/jsac.2017.2676918openalex-snapshot

CAS: Context-Aware Background Application Scheduling in Interactive Mobile Systems

inferred← citeshttps://doi.org/10.1109/percomw.2018.8480308openalex-snapshot

Predicting Contextual Influences on App Usage from a Rational Model of Time Allocation

inferred← citeshttps://doi.org/10.1109/percomw.2019.8730668openalex-snapshot

Estimating User Contexts from Mobile Application Usage Histories

inferred← citeshttps://doi.org/10.1109/qrs-c.2016.24openalex-snapshot

Recommendations Based on LDA Topic Model in Android Applications

inferred← citeshttps://doi.org/10.1109/re48521.2020.00019openalex-snapshot

Which App Features Are Being Used? Learning App Feature Usages from Interaction Data

inferred← citeshttps://doi.org/10.1109/rtcsa.2019.8864577openalex-snapshot

Learning-Assisted Write Latency Optimization for Mobile Storage

inferred← citeshttps://doi.org/10.1109/sahcn.2018.8397106openalex-snapshot

AHT: Application-Based Handover Triggering for Saving Energy in Cellular Networks

inferred← citeshttps://doi.org/10.1109/setit54465.2022.9875502openalex-snapshot

User Group Profiling through Mobile Application Usage Behavior

inferred← citeshttps://doi.org/10.1109/tc.2015.2428692openalex-snapshot

MARS: Mobile Application Relaunching Speed-Up through Flash-Aware Page Swapping

inferred← citeshttps://doi.org/10.1109/thms.2018.2868806openalex-snapshot

Click Sequence Prediction in Android Mobile Applications

inferred← citeshttps://doi.org/10.1109/thms.2024.3412273openalex-snapshot

BERT-Based Semantic-Aware Heterogeneous Graph Embedding Method for Enhancing App Usage Prediction Accuracy

inferred← citeshttps://doi.org/10.1109/ths.2013.6699045openalex-snapshot

A new mobile biometric based upon usage context

inferred← citeshttps://doi.org/10.1109/tmc.2018.2797937openalex-snapshot

Selecting Individual and Population Models for Predicting Human Mobility

inferred← citeshttps://doi.org/10.1109/tmc.2019.2929388openalex-snapshot

Personalized Mobile App Recommendation by Learning User's Interest from Social Media

inferred← citeshttps://doi.org/10.1109/tmc.2021.3093619openalex-snapshot

DeepAPP: A Deep Reinforcement Learning Framework for Mobile Application Usage Prediction

inferred← citeshttps://doi.org/10.1109/tmc.2021.3098664openalex-snapshot

Understanding the Long-Term Evolution of Mobile App Usage

inferred← citeshttps://doi.org/10.1109/tmc.2022.3161114openalex-snapshot

Learning Dynamic App Usage Graph for Next Mobile App Recommendation

inferred← citeshttps://doi.org/10.1109/tmc.2025.3532992openalex-snapshot

Predicting Mobile App Usage With Context-Aware Dynamic Hypergraphs

inferred← citeshttps://doi.org/10.1109/tmc.2025.3605939openalex-snapshot

AppGen: Mobility-Aware App Usage Behavior Generation for Mobile Users

inferred← citeshttps://doi.org/10.1109/tmc.2026.3691045openalex-snapshot

IntentApp: Multi-Granularity Intent-Aware Mobile Application Usage Prediction

inferred← citeshttps://doi.org/10.1109/tnet.2017.2681121openalex-snapshot

Socially-Driven Learning-Based Prefetching in Mobile Online Social Networks

inferred← citeshttps://doi.org/10.1109/tnnls.2015.2462721openalex-snapshot

Sequence Prediction With Sparse Distributed Hyperdimensional Coding Applied to the Analysis of Mobile Phone Use Patterns

inferred← citeshttps://doi.org/10.1109/tnsm.2020.3037503openalex-snapshot

Understanding Data Usage Patterns of Geographically Diverse Mobile Users

inferred← citeshttps://doi.org/10.1109/trustcom/bigdatase/icess.2017.312openalex-snapshot

HiNextApp: A Context-Aware and Adaptive Framework for App Prediction in Mobile Systems

inferred← citeshttps://doi.org/10.1109/tse.2017.2685387openalex-snapshot

Understanding Diverse Usage Patterns from Large-Scale Appstore-Service Profiles

inferred← citeshttps://doi.org/10.1109/tse.2019.2943554openalex-snapshot

FutureWare: Designing a Middleware for Anticipatory Mobile Computing

inferred← citeshttps://doi.org/10.1109/tse.2021.3071193openalex-snapshot

A Survey of Performance Optimization for Mobile Applications

inferred← citeshttps://doi.org/10.1109/uic-atc-scalcom-cbdcom-iop.2015.54openalex-snapshot

Guardian Angel: A Smartphone Based Personal Security System for Emergency Alerting

inferred← citeshttps://doi.org/10.1109/wf-iot.2018.8355155openalex-snapshot

Sequential context modeling for smart devices by Collaborative Hidden Markov Model

inferred← citeshttps://doi.org/10.1109/wimob.2016.7763226openalex-snapshot

iRAM: Sensing memory needs of my smartphone

inferred← citeshttps://doi.org/10.1117/12.2279061openalex-snapshot

Streamlining machine learning in mobile devices for remote sensing

inferred← citeshttps://doi.org/10.1145/2449396.2449431openalex-snapshot

AppFunnel

inferred← citeshttps://doi.org/10.1145/2465351.2465378openalex-snapshot

Prefetching mobile ads

inferred← citeshttps://doi.org/10.1145/2470654.2481294openalex-snapshot

A study on icon arrangement by smartphone users

inferred← citeshttps://doi.org/10.1145/2470654.2481421openalex-snapshot

Bezel-Tap gestures

inferred← citeshttps://doi.org/10.1145/2493190.2493219openalex-snapshot

Oh app, where art thou?

inferred← citeshttps://doi.org/10.1145/2493432.2493490openalex-snapshot

Practical prediction and prefetch for faster access to applications on mobile phones

inferred← citeshttps://doi.org/10.1145/2493988.2494333openalex-snapshot

Preference, context and communities

inferred← citeshttps://doi.org/10.1145/2494091.2494146openalex-snapshot

Prophet

inferred← citeshttps://doi.org/10.1145/2505515.2505529openalex-snapshot

On mining mobile apps usage behavior for predicting apps usage in smartphones

inferred← citeshttps://doi.org/10.1145/2517351.2517372openalex-snapshot

Piggyback CrowdSensing (PCS)

inferred← citeshttps://doi.org/10.1145/2535372.2535391openalex-snapshot

Capturing mobile experience in the wild

inferred← citeshttps://doi.org/10.1145/2628363.2628380openalex-snapshot

ProactiveTasks

inferred← citeshttps://doi.org/10.1145/2632048.2632052openalex-snapshot

MobileMiner

inferred← citeshttps://doi.org/10.1145/2632048.2632080openalex-snapshot

SatScore

inferred← citeshttps://doi.org/10.1145/2638404.2638502openalex-snapshot

Predictive, adaptive mobile user interfaces

inferred← citeshttps://doi.org/10.1145/2638728.2641700openalex-snapshot

An analysis of the transitions between mobile application usages based on markov chains

inferred← citeshttps://doi.org/10.1145/2642918.2647355openalex-snapshot

Reflection

inferred← citeshttps://doi.org/10.1145/2668332.2668362openalex-snapshot

Facilitating continued run of sensor data analytics services using user driven proactive memory reclamation scheme

inferred← citeshttps://doi.org/10.1145/2699343.2699365openalex-snapshot

Mobile AD(D)

inferred← citeshttps://doi.org/10.1145/2750858.2804289openalex-snapshot

Need accurate user behaviour?

inferred← citeshttps://doi.org/10.1145/2750858.2805837openalex-snapshot

Understanding usage states on mobile devices

inferred← citeshttps://doi.org/10.1145/2750858.2807542openalex-snapshot

Revisitation analysis of smartphone app use

inferred← citeshttps://doi.org/10.1145/2785830.2785880openalex-snapshot

PassApp

inferred← citeshttps://doi.org/10.1145/2785830.2785891openalex-snapshot

Understanding the Challenges of Mobile Phone Usage Data

inferred← citeshttps://doi.org/10.1145/2815675.2815686openalex-snapshot

Characterizing Smartphone Usage Patterns from Millions of Android Users

inferred← citeshttps://doi.org/10.1145/2818346.2820781openalex-snapshot

Leveraging Behavioral Patterns of Mobile Applications for Personalized Spoken Language Understanding

inferred← citeshttps://doi.org/10.1145/2856127openalex-snapshot

Workload Characterization

inferred← citeshttps://doi.org/10.1145/2935334.2935383openalex-snapshot

Mobile manifestations of alertness

inferred← citeshttps://doi.org/10.1145/2935755.2935761openalex-snapshot

The Need to Account for Geographical Diversities in Mobile Data Research

inferred← citeshttps://doi.org/10.1145/2948649.2948656openalex-snapshot

Prediction of user app usage behavior from geo-spatial data

inferred← citeshttps://doi.org/10.1145/2968219.2971424openalex-snapshot

Activity sense organs

inferred← citeshttps://doi.org/10.1145/2971648.2971649openalex-snapshot

Looking back on the current day

inferred← citeshttps://doi.org/10.1145/2971648.2971669openalex-snapshot

Modelling smartphone usage

inferred← citeshttps://doi.org/10.1145/2971648.2971680openalex-snapshot

Context-aware application scheduling in mobile systems

inferred← citeshttps://doi.org/10.1145/2971648.2971696openalex-snapshot

Discovering different kinds of smartphone users through their application usage behaviors

inferred← citeshttps://doi.org/10.1145/2971648.2971729openalex-snapshot

A contextual collaborative approach for app usage forecasting

inferred← citeshttps://doi.org/10.1145/3061639.3062317openalex-snapshot

SmartSwap

inferred← citeshttps://doi.org/10.1145/3086665openalex-snapshot

Search, Mining, and Their Applications on Mobile Devices

inferred← citeshttps://doi.org/10.1145/3090052openalex-snapshot

Every Byte Counts

inferred← citeshttps://doi.org/10.1145/3099585openalex-snapshot

Polymodal Menus

inferred← citeshttps://doi.org/10.1145/3126858.3131603openalex-snapshot

Understanding Mobile Application Usage During the 2014 FIFA World Cup

inferred← citeshttps://doi.org/10.1145/3130945openalex-snapshot

TestAWARE

inferred← citeshttps://doi.org/10.1145/3130947openalex-snapshot

Moving Beyond Market Research

inferred← citeshttps://doi.org/10.1145/3130948openalex-snapshot

MyTraces

inferred← citeshttps://doi.org/10.1145/3144730.3144740openalex-snapshot

CAMDroid

inferred← citeshttps://doi.org/10.1145/3154979.3155003openalex-snapshot

Workload Characterization

inferred← citeshttps://doi.org/10.1145/3161175openalex-snapshot

Warming Up to Cold Start Personalization

inferred← citeshttps://doi.org/10.1145/3161413openalex-snapshot

Smartphone App Usage Prediction Using Points of Interest

inferred← citeshttps://doi.org/10.1145/3172944.3172975openalex-snapshot

Cloud Menus

inferred← citeshttps://doi.org/10.1145/3173574.3173918openalex-snapshot

A Large-Scale Study of iPhone App Launch Behaviour

inferred← citeshttps://doi.org/10.1145/3180374.3181345openalex-snapshot

Predict what app to use next time only consider time and latest used app context

inferred← citeshttps://doi.org/10.1145/3191767openalex-snapshot

RuleSelector

inferred← citeshttps://doi.org/10.1145/3209669openalex-snapshot

Discovering Mobile Application Usage Patterns from a Large-Scale Dataset

inferred← citeshttps://doi.org/10.1145/3231884.3231897openalex-snapshot

Improving Application Launch Performance in Smartphones Using Recurrent Neural Network

inferred← citeshttps://doi.org/10.1145/3237190openalex-snapshot

Exploring a Design Space of Graphical Adaptive Menus

inferred← citeshttps://doi.org/10.1145/3264948openalex-snapshot

Your Apps Give You Away

inferred← citeshttps://doi.org/10.1145/3274370openalex-snapshot

How Much is 'Too Much'?

inferred← citeshttps://doi.org/10.1145/3282894.3282908openalex-snapshot

The Context Matters

inferred← citeshttps://doi.org/10.1145/3290605.3300805openalex-snapshot

MessageOnTap

inferred← citeshttps://doi.org/10.1145/3291842.3291909openalex-snapshot

Predicting App Usage from Mobile Internet Traffic Data Based on Node and Path Similarity

inferred← citeshttps://doi.org/10.1145/3292500.3330669openalex-snapshot

Real-time On-Device Troubleshooting Recommendation for Smartphones

inferred← citeshttps://doi.org/10.1145/3314391openalex-snapshot

CAP

inferred← citeshttps://doi.org/10.1145/3314413openalex-snapshot

From Fingerprint to Footprint

inferred← citeshttps://doi.org/10.1145/3319499.3328237openalex-snapshot

Interface adaptivity by widget promotion/demotion

inferred← citeshttps://doi.org/10.1145/3340631.3394849openalex-snapshot

Cohort Modeling Based App Category Usage Prediction

inferred← citeshttps://doi.org/10.1145/3356250.3360038openalex-snapshot

DeepAPP

inferred← citeshttps://doi.org/10.1145/3369829openalex-snapshot

Personalized Context-aware Collaborative Online Activity Prediction

inferred← citeshttps://doi.org/10.1145/3380990openalex-snapshot

PrivateBus

inferred← citeshttps://doi.org/10.1145/3411817openalex-snapshot

Semantic-aware Spatio-temporal App Usage Representation via Graph Convolutional Network

inferred← citeshttps://doi.org/10.1145/3447991openalex-snapshot

Understanding, Discovering, and Mitigating Habitual Smartphone Use in Young Adults

inferred← citeshttps://doi.org/10.1145/3451396openalex-snapshot

App2Vec: Context-Aware Application Usage Prediction

inferred← citeshttps://doi.org/10.1145/3459637.3482076openalex-snapshot

CoSEM: Contextual and Semantic Embedding for App Usage Prediction

inferred← citeshttps://doi.org/10.1145/3460231.3474264openalex-snapshot

Large-Scale Modeling of Mobile User Click Behaviors Using Deep Learning

inferred← citeshttps://doi.org/10.1145/3472749.3474808openalex-snapshot

App-Based Task Shortcuts for Virtual Assistants

inferred← citeshttps://doi.org/10.1145/3491102.3517723openalex-snapshot

Exploring Spatial UI Transition Mechanisms with Head-Worn Augmented Reality

inferred← citeshttps://doi.org/10.1145/3522711openalex-snapshot

Characterization and Prediction of Mobile Tasks

inferred← citeshttps://doi.org/10.1145/3526114.3558714openalex-snapshot

LV-Linker: Supporting Fine-grained User Interaction Analyses by Linking Smartphone Log and Recorded Video Data

showing the first 200 of 258

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.