Chao Huang

Dynamic Representation Learning for Large-Scale Attributed Networks

2020

Network embedding, which aims at learning low-dimensional representations of nodes in a network, has drawn much attention for various network mining tasks, ranging from link prediction to node classification. In addition to network topological information, …

Graph-Enhanced Multi-Task Learning of Multi-Level Transition Dynamics for Session-based Recommendation

2021 · arXiv (Cornell University)

Session-based recommendation plays a central role in a wide spectrum of online applications, ranging from e-commerce to online advertising services. However, the majority of existing session-based recommendation techniques (e.g., attention-based recurrent network or graph neural …

Graph Meta Network for Multi-Behavior Recommendation

2021

Modern recommender systems often embed users and items into low-dimensional latent representations, based on their observed interactions. In practical recommendation scenarios, users often exhibit various intents which drive them to interact with items with multiple …

Synthesized Trust Learning from Limited Human Feedback for Human-Load-Reduced Multi-Robot Deployments

2021

Human multi-robot system (MRS) collaboration is demonstrating potentials in wide application scenarios due to the integration of human cognitive skills and a robot team’s powerful capability introduced by its multi-member structure. However, due to limited …

Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation

2021 · Proceedings of the AAAI Conference on Artificial Intelligence

Accurate user and item embedding learning is crucial for modern recommender systems. However, most existing recommendation techniques have thus far focused on modeling users' preferences over singular type of user-item interactions. Many practical recommendation scenarios …

Design-while-verify

2022

In the current control design of safety-critical cyber-physical systems, formal verification techniques are typically applied after the controller is designed to evaluate whether the required properties (e.g., safety) are satisfied. However, due to the increasing …

Efficient Global Robustness Certification of Neural Networks via Interleaving Twin-Network Encoding

2022 · arXiv (Cornell University)

The robustness of deep neural networks has received significant interest recently, especially when being deployed in safety-critical systems, as it is important to analyze how sensitive the model output is under input perturbations. While most …

Automated Self-Supervised Learning for Recommendation

2023

Graph neural networks (GNNs) have emerged as the state-of-the-art paradigm for collaborative filtering (CF). To improve the representation quality over limited labeled data, contrastive learning has attracted attention in recommendation and benefited graph-based CF model …

Disentangled Graph Social Recommendation

2023

Social recommender systems have drawn a lot of attention in many online web services, because of the incorporation of social information between users in improving recommendation results. Despite the significant progress made by existing solutions, …

Price of Stability in Quality-Aware Federated Learning

2023

Federated Learning (FL) is a distributed machine learning scheme that enables clients to train a shared global model without exchanging local data. The presence of label noise can severely degrade the FL performance, and some …

Revisiting a Pain in the Neck: Semantic Phrase Processing Benchmark for Language Models

2024 · arXiv (Cornell University)

We introduce LexBench, a comprehensive evaluation suite enabled to test language models (LMs) on ten semantic phrase processing tasks. Unlike prior studies, it is the first work to propose a framework from the comparative perspective …

Indicator mineral characteristics of potassic alteration zones in porphyry copper deposits based on infrared spectroscopy technology: A case study of the Qulong porphyry copper deposit, Tibet

2025 · Ore Geology Reviews

• The crystallinity of gypsum(1940 nm/ 1900 nm) plays a crucial role in distinguishing between different types of gypsum. • Type II hydrous gypsum and anhydrite are closely related to mineralization processes in geological systems. …

Online Purchase Prediction via Multi-Scale Modeling of Behavior Dynamics

2019

Online purchase forecasting is of great importance in e-commerce platforms, which is the basis of how to present personalized interesting product lists to individual customers. However, predicting online purchases is not trivial as it is …

Knowledge-aware Coupled Graph Neural Network for Social Recommendation

2021 · Proceedings of the AAAI Conference on Artificial Intelligence

Social recommendation task aims to predict users' preferences over items with the incorporation of social connections among users, so as to alleviate the sparse issue of collaborative filtering. While many recent efforts show the effectiveness …

Hypergraph Contrastive Collaborative Filtering

2022 · Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Collaborative Filtering (CF) has emerged as fundamental paradigms for parameterizing users and items into latent representation space, with their correlative patterns from interaction data. Among various CF techniques, the development of GNN-based recommender systems, e.g., …

Knowledge Graph Contrastive Learning for Recommendation

2022 · Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Knowledge Graphs (KGs) have been utilized as useful side information to improve recommendation quality. In those recommender systems, knowledge graph information often contains fruitful facts and inherent semantic relatedness among items. However, the success of …

LLMRec: Large Language Models with Graph Augmentation for Recommendation

2024

The problem of data sparsity has long been a challenge in recommendation systems, and previous studies have attempted to address this issue by incorporating side information. However, this approach often introduces side effects such as …