48 citations · 106 across the 11 of their papers we have counts for
5 papers · 1 filter
Towards Fine-Grained Explainability for Heterogeneous Graph Neural Network
Tong Li, Jiale Deng, Yanyan Shen +3
Heterogeneous graph neural networks (HGNs) are prominent approaches to node classification tasks on heterogeneous graphs. Despite the superior performance, insights about the predi…
SSIN: Self-Supervised Learning for Rainfall Spatial Interpolation
Jia Li, Yanyan Shen, Lei Chen +1
The acquisition of accurate rainfall distribution in space is an important task in hydrological analysis and natural disaster pre-warning. However, it is impossible to install rain…
Differentiable Neural Input Search for Recommender Systems
Weiyu Cheng, Yanyan Shen, Linpeng Huang
Latent factor models are the driving forces of the state-of-the-art recommender systems, with an important insight of vectorizing raw input features into dense embeddings. The dime…
Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions
Weiyu Cheng, Yanyan Shen, Linpeng Huang
Various factorization-based methods have been proposed to leverage second-order, or higher-order cross features for boosting the performance of predictive models. They generally en…
Explaining Latent Factor Models for Recommendation with Influence Functions
Weiyu Cheng, Yanyan Shen, Yanmin Zhu +1
Latent factor models (LFMs) such as matrix factorization achieve the state-of-the-art performance among various Collaborative Filtering (CF) approaches for recommendation. Despite…