18 citations · 24 across the 4 of their papers we have counts for
4 papers
Incremental Learning for Personalized Recommender Systems
Yunbo Ouyang, Jun Shi, Haichao Wei +1
Ubiquitous personalized recommender systems are built to achieve two seemingly conflicting goals, to serve high quality content tailored to individual user's taste and to adapt qui…
Logit Attenuating Weight Normalization
Aman Gupta, Rohan Ramanath, Jun Shi +4
Over-parameterized deep networks trained using gradient-based optimizers are a popular choice for solving classification and ranking problems. Without appropriately tuned …
Causal Incremental Graph Convolution for Recommender System Retraining
Sihao Ding, Fuli Feng, Xiangnan He +3
Real-world recommender system needs to be regularly retrained to keep with the new data. In this work, we consider how to efficiently retrain graph convolution network (GCN) based…
Memory-efficient Embedding for Recommendations
Xiangyu Zhao, Haochen Liu, Hui Liu +6
Practical large-scale recommender systems usually contain thousands of feature fields from users, items, contextual information, and their interactions. Most of them empirically al…