Hui Xiong

A Survey on Knowledge Graph-Based Recommender Systems

2020 · arXiv (Cornell University)

To solve the information explosion problem and enhance user experience in various online applications, recommender systems have been developed to model users preferences. Although numerous efforts have been made toward more personalized recommendations, recommender systems …

Medical Entity Relation Verification with Large-scale Machine Reading Comprehension

2021

Medical entity relation verification is a crucial step to build a practical and enterprise medical knowledge graph (MKG) because high-precision medical entity relation is a key requirement for many MKG-based applications. Existing relation verification approaches …

Out-of-Town Recommendation with Travel Intention Modeling

2021 · Proceedings of the AAAI Conference on Artificial Intelligence

Out-of-town recommendation is designed for those users who leave their home-town areas and visit the areas they have never been to before. It is challenging to recommend Point-of-Interests (POIs) for out-of-town users since the out-of-town …

Towards Robust Knowledge Graph Embedding via Multi-Task Reinforcement Learning

2021 · IEEE Transactions on Knowledge and Data Engineering

Nowadays, Knowledge graphs (KGs) have been playing a pivotal role in AI-related applications. Despite the large sizes, existing KGs are far from complete and comprehensive. In order to continuously enrich KGs, automatic knowledge construction and …

CBR: Context Bias aware Recommendation for Debiasing User Modeling and Click Prediction

2022 · Proceedings of the ACM Web Conference 2022

With the prosperity of recommender systems, the biases existing in user behaviors, which may lead to inconsistency between user preference and behavior records, have attracted wide attention. Though large efforts have been made to infer …

Seq-HGNN: Learning Sequential Node Representation on Heterogeneous Graph

2023

Recent years have witnessed the rapid development of heterogeneous graph neural networks (HGNNs) in information retrieval (IR) applications. Many existing HGNNs design a variety of tailor-made graph convolutions to capture structural and semantic information in …

A Survey on Knowledge Graph-Based Recommender Systems : Extended Abstract

2023

To solve the information explosion problem and enhance user experience in various online applications, recommender systems have been developed to model users’ preferences. Although numerous efforts have been made toward more personalized recommendations, recommender systems …

Towards Faithful Neural Network Intrinsic Interpretation with Shapley Additive Self-Attribution

2023 · arXiv (Cornell University)

Self-interpreting neural networks have garnered significant interest in research. Existing works in this domain often (1) lack a solid theoretical foundation ensuring genuine interpretability or (2) compromise model expressiveness. In response, we formulate a generic …

LLM-Based Agent Society Investigation: Collaboration and Confrontation in Avalon Gameplay

2023 · arXiv (Cornell University)

This paper explores the open research problem of understanding the social behaviors of LLM-based agents. Using Avalon as a testbed, we employ system prompts to guide LLM agents in gameplay. While previous studies have touched …

When Graph Neural Network Meets Causality: Opportunities, Methodologies and An Outlook

2023 · arXiv (Cornell University)

Graph Neural Networks (GNNs) have emerged as powerful representation learning tools for capturing complex dependencies within diverse graph-structured data. Despite their success in a wide range of graph mining tasks, GNNs have raised serious concerns …

Collaboration-Aware Hybrid Learning for Knowledge Development Prediction

2024

In recent years, the rise of online Knowledge Management Systems (KMSs) has significantly improved work efficiency in enterprises. Knowledge development prediction, as a critical application within these online platforms, enables organizations to proactively address knowledge …

ScIRGen: Synthesize Realistic and Large-Scale RAG Dataset for Scientific Research

2025

Scientific researchers need intensive information about datasets to effectively evaluate and develop theories and methodologies. The information needs regarding datasets are implicitly embedded in particular research tasks, rather than explicitly expressed in search queries. However, …

LLM-Oriented Information Retrieval: A Denoising-First Perspective

2026 · Rare & Special e-Zone (The Hong Kong University of Science and Technology)

Modern information retrieval (IR) is no longer consumed primarily by humans but increasingly by large language models (LLMs) via retrieval-augmented generation (RAG) and agentic search. Unlike human users, LLMs are constrained by limited attention budgets …

POI Recommendation: A Temporal Matching between POI Popularity and User Regularity

2016

Point of interest (POI) recommendation, which provides personalized recommendation of places to mobile users, is an important task in location-based social networks (LBSNs). However, quite different from traditional interest-oriented merchandise recommendation, POI recommendation is more …

Learning to Recommend Accurate and Diverse Items

2017

In this study, we investigate diversified recommendation problem by supervised learning, seeking significant improvement in diversity while maintaining accuracy. In particular, we regard each user as a training instance, and heuristically choose a subset of …

Sequential Recommender System based on Hierarchical Attention Networks

2018

With a large amount of user activity data accumulated, it is crucial to exploit user sequential behavior for sequential recommendations. Conventionally, user general taste and recent demand are combined to promote recommendation performances. However, existing …

Person-Job Fit

2018 · ACM Transactions on Management Information Systems

Person-Job Fit is the process of matching the right talent for the right job by identifying talent competencies that are required for the job. While many qualitative efforts have been made in related fields, it …

Recurrent Convolutional Neural Network for Sequential Recommendation

2019

The sequential recommendation, which models sequential behavioral patterns among users for the recommendation, plays a critical role in recommender systems. However, the state-of-the-art Recurrent Neural Networks (RNN) solutions rarely consider the non-linear feature interactions and …