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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-642-19309-5_55openalex-snapshot
Cosine Similarity Metric Learning for Face Verification
inferredcites →https://doi.org/10.1007/s10618-014-0343-4openalex-snapshot
Addressing the cold-start problem in location recommendation using geo-social correlations
inferredcites →https://doi.org/10.1109/icde.2019.00140openalex-snapshot
Neural Multi-task Recommendation from Multi-behavior Data
inferredcites →https://doi.org/10.1109/icdm.2012.31openalex-snapshot
Mining Personal Context-Aware Preferences for Mobile Users
inferredcites →https://doi.org/10.1109/mc.2009.263openalex-snapshot
Matrix Factorization Techniques for Recommender Systems
inferredcites →https://doi.org/10.1109/smartcomp.2014.7043857openalex-snapshot
Demographic information prediction based on smartphone application usage
inferredcites →https://doi.org/10.1109/tkde.2015.2508816openalex-snapshot
Connecting Social Media to E-Commerce: Cold-Start Product Recommendation Using Microblogging Information
inferredcites →https://doi.org/10.1142/s0219622013500077openalex-snapshot
A SURVEY OF CONTEXT-AWARE MOBILE RECOMMENDATIONS
inferredcites →https://doi.org/10.1145/1401890.1401969openalex-snapshot
Relational learning via collective matrix factorization
inferredcites →https://doi.org/10.1145/1814433.1814453openalex-snapshot
Diversity in smartphone usage
inferredcites →https://doi.org/10.1145/1935826.1935877openalex-snapshot
Recommender systems with social regularization
inferredcites →https://doi.org/10.1145/2093973.2093980openalex-snapshot
Semantic trajectory mining for location prediction
inferredcites →https://doi.org/10.1145/2370216.2370243openalex-snapshot
Understanding and prediction of mobile application usage for smart phones
inferredcites →https://doi.org/10.1145/2370216.2370442openalex-snapshot
Predicting mobile application usage using contextual information
inferredcites →https://doi.org/10.1145/2492517.2492652openalex-snapshot
A HITS-based POI recommendation algorithm for location-based social networks
inferredcites →https://doi.org/10.1145/2493988.2494333openalex-snapshot
Preference, context and communities
inferredcites →https://doi.org/10.1145/2600428.2609599openalex-snapshot
Addressing cold start in recommender systems
inferredcites →https://doi.org/10.1145/2628363.2628367openalex-snapshot
Contextual experience sampling of mobile application micro-usage
inferredcites →https://doi.org/10.1145/2645710.2645751openalex-snapshot
Item cold-start recommendations
inferredcites →https://doi.org/10.1145/2645710.2645772openalex-snapshot
Social collaborative filtering for cold-start recommendations
inferredcites →https://doi.org/10.1145/2750858.2807542openalex-snapshot
Revisitation analysis of smartphone app use
inferredcites →https://doi.org/10.1145/2785830.2785891openalex-snapshot
Understanding the Challenges of Mobile Phone Usage Data
inferredcites →https://doi.org/10.1145/2948649.2948656openalex-snapshot
Prediction of user app usage behavior from geo-spatial data
inferredcites →https://doi.org/10.1145/2971648.2971673openalex-snapshot
Context-aware real-time population estimation for metropolis
inferredcites →https://doi.org/10.1145/2971648.2971696openalex-snapshot
Discovering different kinds of smartphone users through their application usage behaviors
inferredcites →https://doi.org/10.1145/2971648.2971707openalex-snapshot
Differentiating smartphone users by app usage
inferredcites →https://doi.org/10.1145/2983323.2983711openalex-snapshot
Learning Graph-based POI Embedding for Location-based Recommendation
inferredcites →https://doi.org/10.1145/3161200openalex-snapshot
Understanding Group Event Scheduling via the OutWithFriendz Mobile Application
inferredcites →https://doi.org/10.1145/3161413openalex-snapshot
Smartphone App Usage Prediction Using Points of Interest
inferredcites →https://doi.org/10.1145/3269206.3269234openalex-snapshot
Recommender Systems with Characterized Social Regularization
inferredcites →https://doi.org/10.1609/aaai.v24i1.7577openalex-snapshot
Collaborative Filtering Meets Mobile Recommendation: A User-Centered Approach
inferredcites →https://doi.org/10.1609/aaai.v30i1.9971openalex-snapshot
Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts
inferredcites →https://doi.org/10.1609/aaai.v30i1.9986openalex-snapshot
STELLAR: Spatial-Temporal Latent Ranking for Successive Point-of-Interest Recommendation
inferredcites →https://doi.org/10.1609/icwsm.v10i1.14776openalex-snapshot
You Are What Apps You Use: Demographic Prediction Based on User's Apps
inferredcites →https://doi.org/10.21236/ada439541openalex-snapshot
Application of Dimensionality Reduction in Recommender System - A Case Study
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-981-99-3925-1_5openalex-snapshot
Location Recommendations Based on Multi-view Learning and Attention-Enhanced Graph Networks
inferred← citeshttps://doi.org/10.1007/s42486-019-00011-4openalex-snapshot
Investigating smartphone user differences in their application usage behaviors: an empirical study
inferred← citeshttps://doi.org/10.1016/j.trc.2022.103921openalex-snapshot
Traffic prediction using artificial intelligence: Review of recent advances and emerging opportunities
inferred← citeshttps://doi.org/10.1109/icws67624.2025.00095openalex-snapshot
POI-Based Edge Service Deployment With Topology -Aware Optimization
inferred← citeshttps://doi.org/10.1109/jsac.2020.2971896openalex-snapshot
Mobile App Usage Patterns Aware Smart Data Pricing
inferred← citeshttps://doi.org/10.1109/tbdata.2022.3204759openalex-snapshot
Modeling Spatial Trajectories Using Coarse-Grained Smartphone Logs
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/tnsm.2021.3112702openalex-snapshot
Recommendation Model Based on Dynamic Interest Group Identification and Data Compensation
inferred← citeshttps://doi.org/10.1145/3314414openalex-snapshot
Modeling Spatio-Temporal App Usage for a Large User Population
inferred← citeshttps://doi.org/10.1145/3369822openalex-snapshot
Learning from Hometown and Current City
inferred← citeshttps://doi.org/10.1145/3369829openalex-snapshot
Personalized Context-aware Collaborative Online Activity Prediction
inferred← citeshttps://doi.org/10.1145/3394171.3413628openalex-snapshot
How to Learn Item Representation for Cold-Start Multimedia Recommendation?
inferred← citeshttps://doi.org/10.1145/3411817openalex-snapshot
Semantic-aware Spatio-temporal App Usage Representation via Graph Convolutional Network
inferred← citeshttps://doi.org/10.1145/3491102.3517459openalex-snapshot
Automatically Generating and Improving Voice Command Interface from Operation Sequences on Smartphones
inferred← citeshttps://doi.org/10.1145/3627673.3679893openalex-snapshot
CrossPred: A Cross-City Mobility Prediction Framework for Long-Distance Travelers via POI Feature Matching
inferred← citeshttps://doi.org/10.1145/3774935.3806192openalex-snapshot
Language Embeddings Meet Shallow Autoencoders
inferred← citeshttps://doi.org/10.31763/sitech.v2i1.610openalex-snapshot
Location-Aware Recommender System: A review of Application Domains and Current Developmental Processes
inferred← citeshttps://doi.org/10.3390/app11209608openalex-snapshot
Eliciting Auxiliary Information for Cold Start User Recommendation: A Survey
inferred← citeshttps://doi.org/10.3390/app12146882openalex-snapshot
Resolving Data Sparsity via Aggregating Graph-Based User–App–Location Association for Location Recommendations
inferred← citeshttps://doi.org/10.3390/math8122138openalex-snapshot
On the Smaller Number of Inputs for Determining User Preferences in Recommender Systems
inferred← citeshttps://doi.org/10.48550/arxiv.2101.05641openalex-snapshot
$C^3DRec$: Cloud-Client Cooperative Deep Learning for Temporal Recommendation in the Post-GDPR Era
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