On the public citation graph
Dynamic User Interest Augmentation via Stream Clustering and Memory Networks in Large-Scale Recommender Systems
0 signed · 34 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.
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known work and none has been retracted — and get a dated CiteStamped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.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/3-540-44886-1_25openalex-extracted
AUC: A Better Measure than Accuracy in Comparing Learning Algorithms
inferredcites →https://doi.org/10.1007/978-3-031-30678-5_40openalex-extracted
Cold & Warm Net: Addressing Cold-Start Users in Recommender Systems
inferredcites →https://doi.org/10.1109/icdm.2016.0110openalex-extracted
HLGPS: A Home Location Global Positioning System in Location-Based Social Networks
inferredcites →https://doi.org/10.1109/jproc.2020.3004555openalex-extracted
A Comprehensive Survey on Transfer Learning
inferredcites →https://doi.org/10.1109/mci.2024.3363984openalex-extracted
Recent Developments in Recommender Systems: A Survey [Review Article]
inferredcites →https://doi.org/10.1109/mic.2003.1167344openalex-extracted
Amazon.com recommendations: item-to-item collaborative filtering
inferredcites →https://doi.org/10.1145/2959100.2959190openalex-extracted
Deep Neural Networks for YouTube Recommendations
inferredcites →https://doi.org/10.1145/3219819.3219823openalex-extracted
Deep Interest Network for Click-Through Rate Prediction
inferredcites →https://doi.org/10.1145/3219819.3220007openalex-extracted
Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts
inferredcites →https://doi.org/10.1145/3285029openalex-extracted
Deep Learning Based Recommender System
inferredcites →https://doi.org/10.1145/3292500.3330668openalex-extracted
Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems
inferredcites →https://doi.org/10.1145/3298689.3346997openalex-extracted
Recommending what video to watch next
inferredcites →https://doi.org/10.1145/3336191.3371827openalex-extracted
Hierarchical User Profiling for E-commerce Recommender Systems
inferredcites →https://doi.org/10.1145/3340531.3412744openalex-extracted
Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction
inferredcites →https://doi.org/10.1145/3383313.3412236openalex-extracted
Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations
inferredcites →https://doi.org/10.1145/3404835.3462843openalex-extracted
Learning to Warm Up Cold Item Embeddings for Cold-start Recommendation with Meta Scaling and Shifting Networks
inferredcites →https://doi.org/10.1145/3442381.3450086openalex-extracted
A Model of Two Tales: Dual Transfer Learning Framework for Improved Long-tail Item Recommendation
inferredcites →https://doi.org/10.1145/3447548.3467108openalex-extracted
Sliding Spectrum Decomposition for Diversified Recommendation
inferredcites →https://doi.org/10.1145/3459637.3481952openalex-extracted
Self-supervised Learning for Large-scale Item Recommendations
inferredcites →https://doi.org/10.1145/3477495.3531902openalex-extracted
Improving Item Cold-start Recommendation via Model-agnostic Conditional Variational Autoencoder
inferredcites →https://doi.org/10.1145/3485447.3512048openalex-extracted
Alleviating Cold-start Problem in CTR Prediction with A Variational Embedding Learning Framework
inferredcites →https://doi.org/10.1145/3511808.3557082openalex-extracted
Sampling Is All You Need on Modeling Long-Term User Behaviors for CTR Prediction
inferredcites →https://doi.org/10.1145/3534678.3539040openalex-extracted
Multi-Task Fusion via Reinforcement Learning for Long-Term User Satisfaction in Recommender Systems
inferredcites →https://doi.org/10.1145/3580305.3599884openalex-extracted
PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information
inferredcites →https://doi.org/10.1609/aaai.v33i01.33015941openalex-extracted
Deep Interest Evolution Network for Click-Through Rate Prediction
inferredcites →https://doi.org/10.2139/ssrn.4802791openalex-extracted
UnifiedRL: A Reinforcement Learning Algorithm Tailored for Multi-Task Fusion in Large-Scale Recommender Systems
inferredcites →https://doi.org/10.48550/arxiv.1412.6980openalex-extracted
Adam: A Method for Stochastic Optimization
inferredcites →https://doi.org/10.48550/arxiv.1904.11547openalex-extracted
Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings
inferredcites →https://doi.org/10.48550/arxiv.1908.00413openalex-extracted
MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation
inferredcites →https://doi.org/10.48550/arxiv.2006.05639openalex-extracted
Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction
inferredcites →https://doi.org/10.48550/arxiv.2108.04690openalex-extracted
POSO: Personalized Cold Start Modules for Large-scale Recommender Systems
inferredcites →https://doi.org/10.48550/arxiv.2209.07663openalex-extracted
Monolith: Real Time Recommendation System With Collisionless Embedding Table
inferredcites →https://doi.org/10.48550/arxiv.2306.12680openalex-extracted
Recent Developments in Recommender Systems: A Survey
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.