Xiangliang Zhang

Graph Embedding for Recommendation against Attribute Inference Attacks

2021

In recent years, recommender systems play a pivotal role in helping users identify the most suitable items that satisfy personal preferences. As user-item interactions can be naturally modelled as graph-structured data, variants of graph convolutional …

Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation

2021 · Proceedings of the AAAI Conference on Artificial Intelligence

Session-based recommendation (SBR) focuses on next-item prediction at a certain time point. As user profiles are generally not available in this scenario, capturing the user intent lying in the item transitions plays a pivotal role. …

Unsupervised Mitigating Gender Bias by Character Components: A Case Study of Chinese Word Embedding

2022

Word embeddings learned from massive text collections have demonstrated significant levels of discriminative biases. However, debiasing on the Chinese language, one of the most spoken languages, has been less explored. Meanwhile, existing literature relies on …

TAR: Neural Logical Reasoning across TBox and ABox

2022 · arXiv (Cornell University)

Many ontologies, i.e., Description Logic (DL) knowledge bases, have been developed to provide rich knowledge about various domains. An ontology consists of an ABox, i.e., assertion axioms between two entities or between a concept and …

1+1>2: Can Large Language Models Serve as Cross-Lingual Knowledge Aggregators?

2024 · arXiv (Cornell University)

Large Language Models (LLMs) have garnered significant attention due to their remarkable ability to process information across various languages. Despite their capabilities, they exhibit inconsistencies in handling identical queries in different languages, presenting challenges for …

Research on Multi-Agent Competition Based on Large Language Models

2025

Large Language Models (LLMs), with their advanced capabilities in semantic understanding, dynamic strategy generation, and complex behavior simulation, represent a transformative tool for studying multi-agent interactions. Their exceptional performance in areas such as natural language …

Better Datasets Start From RefineLab: Automatic Optimization for High-Quality Dataset Refinement

2025 · arXiv (Cornell University)

High-quality Question-Answer (QA) datasets are foundational for reliable Large Language Model (LLM) evaluation, yet even expert-crafted datasets exhibit persistent gaps in domain coverage, misaligned difficulty distributions, and factual inconsistencies. The recent surge in generative model-powered …

Co-Embedding Attributed Networks

2019

Existing embedding methods for attributed networks aim at learning low-dimensional vector representations for nodes only but not for both nodes and attributes, resulting in the fact that they cannot capture the affinities between nodes and …