49 citations · 169 across the 12 of their papers we have counts for
23 papers
Conditional Attention Networks for Distilling Knowledge Graphs in Recommendation
Ke Tu, Peng Cui, Daixin Wang +4
Knowledge graph is generally incorporated into recommender systems to improve overall performance. Due to the generalization and scale of the knowledge graph, most knowledge relati…
SHORING: Design Provable Conditional High-Order Interaction Network via Symbolic Testing
Hui Li, Xing Fu, Ruofan Wu +8
Deep learning provides a promising way to extract effective representations from raw data in an end-to-end fashion and has proven its effectiveness in various domains such as compu…
Bandit Samplers for Training Graph Neural Networks
Ziqi Liu, Zhengwei Wu, Zhiqiang Zhang +4
Several sampling algorithms with variance reduction have been proposed for accelerating the training of Graph Convolution Networks (GCNs). However, due to the intractable computati…
SpellGCN: Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling Check
Xingyi Cheng, Weidi Xu, Kunlong Chen +5
Chinese Spelling Check (CSC) is a task to detect and correct spelling errors in Chinese natural language. Existing methods have made attempts to incorporate the similarity knowledg…
Practical Privacy Preserving POI Recommendation
Chaochao Chen, Jun Zhou, Bingzhe Wu +4
Point-of-Interest (POI) recommendation has been extensively studied and successfully applied in industry recently. However, most existing approaches build centralized models on the…
RNE: A Scalable Network Embedding for Billion-scale Recommendation
Jianbin Lin, Daixin Wang, Lu Guan +5
Nowadays designing a real recommendation system has been a critical problem for both academic and industry. However, due to the huge number of users and items, the diversity and dy…