28 citations · 150 across the 50 of their papers we have counts for
53 papers
Intent Propagation Contrastive Collaborative Filtering
Haojie Li, Junwei Du, Guanfeng Liu +3
Disentanglement techniques used in collaborative filtering uncover interaction intents between nodes, improving the interpretability of node representations and enhancing recommend…
ID and Graph View Contrastive Learning with Multi-View Attention Fusion for Sequential Recommendation
Xiaofan Zhou, Kyumin Lee
Sequential recommendation has become increasingly prominent in both academia and industry, particularly in e-commerce. The primary goal is to extract user preferences from historic…
CroTad: A Contrastive Reinforcement Learning Framework for Online Trajectory Anomaly Detection
Rui Xue, Dan He, Fengmei Jin +2
Detecting trajectory anomalies is a vital task in modern Intelligent Transportation Systems (ITS), enabling the identification of unsafe, inefficient, or irregular travel behaviour…
Zero-Shot Cellular Trajectory Map Matching
Weijie Shi, Yue Cui, Hao Chen +5
Cellular Trajectory Map-Matching (CTMM) aims to align cellular location sequences to road networks, which is a necessary preprocessing in location-based services on web platforms l…
Beyond Model Base Retrieval: Weaving Knowledge to Master Fine-grained Neural Network Design
Jialiang Wang, Hanmo Liu, Shimin Di +4
Designing high-performance neural networks for new tasks requires balancing optimization quality with search efficiency. Current methods fail to achieve this balance: neural archit…
Unraveling Spatio-Temporal Foundation Models via the Pipeline Lens: A Comprehensive Review
Yuchen Fang, Hao Miao, Yuxuan Liang +9
Spatio-temporal deep learning models aims to utilize useful patterns in such data to support tasks like prediction. However, previous deep learning models designed for specific tas…
Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations
Yuyang Ding, Dan Qiao, Juntao Li +4
Distantly supervised named entity recognition (DS-NER) has emerged as a cheap and convenient alternative to traditional human annotation methods, enabling the automatic generation…
Learning Towards Emergence: Paving the Way to Induce Emergence by Inhibiting Monosemantic Neurons on Pre-trained Models
Jiachuan Wang, Shimin Di, Tianhao Tang +4
Emergence, the phenomenon of a rapid performance increase once the model scale reaches a threshold, has achieved widespread attention recently. The literature has observed that mon…
A Pilot Empirical Study on When and How to Use Knowledge Graphs as Retrieval Augmented Generation
Xujie Yuan, Yongxu Liu, Shimin Di +6
The integration of Knowledge Graphs (KGs) into the Retrieval Augmented Generation (RAG) framework has attracted significant interest, with early studies showing promise in mitigati…
Top Ten Challenges Towards Agentic Neural Graph Databases
Jiaxin Bai, Zihao Wang, Yukun Zhou +16
Graph databases (GDBs) like Neo4j and TigerGraph excel at handling interconnected data but lack advanced inference capabilities. Neural Graph Databases (NGDBs) address this by inte…
Three Pillars Towards Next-Generation Routing System
Lei Li, Mengxuan Zhang, Zizhuo Xu +2
The routing results are playing an increasingly important role in transportation efficiency, but they could generate traffic congestion unintentionally. This is because the traffic…
High Throughput Shortest Distance Query Processing on Large Dynamic Road Networks
Xinjie Zhou, Mengxuan Zhang, Lei Li +1
Shortest path (SP) computation is the building block for many location-based services, and achieving high throughput SP query processing with real-time response is crucial for thos…
Proficient Graph Neural Network Design by Accumulating Knowledge on Large Language Models
Jialiang Wang, Hanmo Liu, Shimin Di +4
High-level automation is increasingly critical in AI, driven by rapid advances in large language models (LLMs) and AI agents. However, LLMs, despite their general reasoning power,…
Unifying Lane-Level Traffic Prediction from a Graph Structural Perspective: Benchmark and Baseline
Shuhao Li, Yue Cui, Jingyi Xu +5
Traffic prediction has long been a focal and pivotal area in research, witnessing both significant strides from city-level to road-level predictions in recent years. With the advan…
Trajectory Data Management and Mining: A Survey from Deep Learning to the LLM Era
Wei Chen, Yuanshao Zhu, Yanchuan Chang +11
Trajectory computing is a pivotal domain encompassing trajectory data management and mining, garnering widespread attention due to its crucial role in various practical application…
Adaptive Hypergraph Network for Trust Prediction
Rongwei Xu, Guanfeng Liu, Yan Wang +3
Trust plays an essential role in an individual's decision-making. Traditional trust prediction models rely on pairwise correlations to infer potential relationships between users.…
Few-Shot Causal Representation Learning for Out-of-Distribution Generalization on Heterogeneous Graphs
Pengfei Ding, Yan Wang, Guanfeng Liu +2
Heterogeneous graph few-shot learning (HGFL) has been developed to address the label sparsity issue in heterogeneous graphs (HGs), which consist of various types of nodes and edges…
Multi-Modality is All You Need for Transferable Recommender Systems
Youhua Li, Hanwen Du, Yongxin Ni +4
ID-based Recommender Systems (RecSys), where each item is assigned a unique identifier and subsequently converted into an embedding vector, have dominated the designing of RecSys.…
Scene-Driven Multimodal Knowledge Graph Construction for Embodied AI
Song Yaoxian, Sun Penglei, Liu Haoyu +4
Embodied AI is one of the most popular studies in artificial intelligence and robotics, which can effectively improve the intelligence of real-world agents (i.e. robots) serving hu…
Meta-optimized Joint Generative and Contrastive Learning for Sequential Recommendation
Yongjing Hao, Pengpeng Zhao, Junhua Fang +5
Sequential Recommendation (SR) has received increasing attention due to its ability to capture user dynamic preferences. Recently, Contrastive Learning (CL) provides an effective a…
A Universal Scheme for Dynamic Partitioned Shortest Path Index: Survey, Improvement, and Experiments
Mengxuan Zhang, Xinjie Zhou, Lei Li +4
Shortest Path (SP) computation is a fundamental operation in many real-life applications such as navigation on road networks, link analysis on social networks, etc. These networks…
Invariant representation learning for sequential recommendation
Xiaofan Zhou
Sequential recommendation involves automatically recommending the next item to users based on their historical item sequence. While most prior research employs RNN or transformer m…
Search to Fine-tune Pre-trained Graph Neural Networks for Graph-level Tasks
Zhili Wang, Shimin Di, Lei Chen +1
Recently, graph neural networks (GNNs) have shown its unprecedented success in many graph-related tasks. However, GNNs face the label scarcity issue as other neural networks do. Th…
HeteFedRec: Federated Recommender Systems with Model Heterogeneity
Wei Yuan, Liang Qu, Lizhen Cui +3
Owing to the nature of privacy protection, federated recommender systems (FedRecs) have garnered increasing interest in the realm of on-device recommender systems. However, most ex…