Search Results for author:
Found 4 papers, 3 papers with code
Dynamic Graph Neural Networks for Sequential Recommendation
We propose a new method named Dynamic Graph Neural Network for Sequential Recommendation (DGSR), which connects different user sequences through a dynamic graph structure, exploring the interactive behavior of users and items with time and order information.
Graph-based Hierarchical Relevance Matching Signals for Ad-hoc Retrieval
In addition, due to the complexity and scale of the document collections, it is considerable to explore the different grain-sized hierarchical matching signals at a more general level.
TFNet: Multi-Semantic Feature Interaction for CTR Prediction
The CTR (Click-Through Rate) prediction plays a central role in the domain of computational advertising and recommender systems.
Ranked #31 on
Click-Through Rate Prediction
on Criteo
Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks
We first build individual graphs for each document and then use GNN to learn the fine-grained word representations based on their local structures, which can also effectively produce embeddings for unseen words in the new document.
