Ling Chen

Recurrent Dirichlet Belief Networks for Interpretable Dynamic Relational Data Modelling

2020 · arXiv (Cornell University)

The Dirichlet Belief Network~(DirBN) has been recently proposed as a promising approach in learning interpretable deep latent representations for objects. In this work, we leverage its interpretable modelling architecture and propose a deep dynamic probabilistic …

DexDeepFM: Ensemble Diversity Enhanced Extreme Deep Factorization Machine Model

2021 · arXiv (Cornell University)

Predicting user positive response (e.g., purchases and clicks) probability is a critical task in Web applications. To identify predictive features from raw data, the state-of-the-art extreme deep factorization machine model (xDeepFM) introduces a new interaction …

DECRL: A Deep Evolutionary Clustering Jointed Temporal Knowledge Graph Representation Learning Approach

2024 · arXiv (Cornell University)

Temporal Knowledge Graph (TKG) representation learning aims to map temporal evolving entities and relations to embedded representations in a continuous low-dimensional vector space. However, existing approaches cannot capture the temporal evolution of high-order correlations in …

DHFM: Diversity Enhanced Hypergraph Factorization Machines for Feature Interaction Modeling

2025 · ACM Transactions on Knowledge Discovery from Data

Feature interaction modeling, which exploits interactive information between features, has been widely explored in various applications. Recently, many graph neural networks (GNNs) based models are proposed to model feature interactions by predicting the existence of …

Spatial-Aware Hierarchical Collaborative Deep Learning for POI Recommendation

2017 · IEEE Transactions on Knowledge and Data Engineering

Point-of-interest (POI) recommendation has become an important way to help people discover attractive and interesting places, especially when they travel out of town. However, the extreme sparsity of user-POI matrix and cold-start issues severely hinder …