35 citations · 112 across the 24 of their papers we have counts for
24 papers
Less or More From Teacher: Exploiting Trilateral Geometry For Knowledge Distillation
Chengming Hu, Haolun Wu, Xuan Li +5
Knowledge distillation aims to train a compact student network using soft supervision from a larger teacher network and hard supervision from ground truths. However, determining an…
Dynamic Embedding Size Search with Minimum Regret for Streaming Recommender System
Bowei He, Xu He, Renrui Zhang +3
With the continuous increase of users and items, conventional recommender systems trained on static datasets can hardly adapt to changing environments. The high-throughput data req…
Collaborative Edge Caching: a Meta Reinforcement Learning Approach with Edge Sampling
Bowei He, Yinan Mao, Shiji Zhou +2
Current learning-based edge caching schemes usually suffer from dynamic content popularity, e.g., in the emerging short video platforms, users' request patterns shift significantly…
Structure Aware Incremental Learning with Personalized Imitation Weights for Recommender Systems
Yuening Wang, Yingxue Zhang, Antonios Valkanas +4
Recommender systems now consume large-scale data and play a significant role in improving user experience. Graph Neural Networks (GNNs) have emerged as one of the most effective re…
Dynamically Expandable Graph Convolution for Streaming Recommendation
Bowei He, Xu He, Yingxue Zhang +2
Personalized recommender systems have been widely studied and deployed to reduce information overload and satisfy users' diverse needs. However, conventional recommendation models…
Feature Representation Learning for Click-through Rate Prediction: A Review and New Perspectives
Fuyuan Lyu, Xing Tang, Dugang Liu +4
Representation learning has been a critical topic in machine learning. In Click-through Rate Prediction, most features are represented as embedding vectors and learned simultaneous…