DEng in Computer Science and Technology
University of Electronic Science and Technology of China, 2023
When large language models meet personalization: perspectives of challenges and opportunities
Article
An Incremental Update Framework for Online Recommenders with Data-Driven Prior
Conference paper
AutoS2AE: Automate to Regularize Sparse Shallow Autoencoders for Recommendation
Conference paper
Batch-Mix Negative Sampling for Learning Recommendation Retrievers
Conference paper
Conference paper
Cooperative Retriever and Ranker in Deep Recommenders
Conference paper
Knowledge Distillation for High Dimensional Search Index
Conference paper
RecStudio: Towards a Highly-Modularized Recommender System
Conference paper
SimiDTR: Deep Trajectory Recovery with Enhanced Trajectory Similarity
Conference paper
Ranking-Based Implicit Regularization for One-Class Collaborative Filtering
Article
Cache-Augmented Inbatch Importance Resampling for Training Recommender Retriever
Conference paper
Efficient Join Order Selection Learning with Graph-based Representation
Conference paper
Efficient Learning with Pseudo Labels for Query Cost Estimation
Conference paper
Fast Variational AutoEncoder with Inverted Multi-Index for Collaborative Filtering
Conference paper
Improving Implicit Alternating Least Squares with Ring-based Regularization
Conference paper
Learning Recommenders for Implicit Feedback with Importance Resampling
Conference paper
Automated creative optimization for E-commerce advertising
Conference paper
Efficient Optimal Selection for Composited Advertising Creatives with Tree Structure
Conference paper
XLightFM: Extremely Memory-Efficient Factorization Machine
Conference paper
Improving one-class collaborative filtering via ranking-based implicit regularizer
Conference paper
An Incremental Update Framework for Online Recommenders with Data-Driven Prior
AutoS2AE: Automate to Regularize Sparse Shallow Autoencoders for Recommendation
Batch-Mix Negative Sampling for Learning Recommendation Retrievers
SimiDTR: Deep Trajectory Recovery with Enhanced Trajectory Similarity
Cache-Augmented Inbatch Importance Resampling for Training Recommender Retriever
Efficient Join Order Selection Learning with Graph-based Representation
Efficient Learning with Pseudo Labels for Query Cost Estimation
Fast Variational AutoEncoder with Inverted Multi-Index for Collaborative Filtering
Improving Implicit Alternating Least Squares with Ring-based Regularization
Learning Recommenders for Implicit Feedback with Importance Resampling
ISOM3000I | Business Algorithms in Python: Theory and Practice |
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