Xiang Wang

Automatic Clustering Algorithm for Movie Recommendation Based on LFM Model

2017 · DEStech Transactions on Computer Science and Engineering

In order to enhance audience satisfaction and recommend them different types of movies, it is necessary to point out the concept, latent factor model (hereinafter referred to as LFM). By using clustering analysis technology, users …

Smart Contract Vulnerability Detection using Graph Neural Network

2020

The security problems of smart contracts have drawn extensive attention due to the enormous financial losses caused by vulnerabilities. Existing methods on smart contract vulnerability detection heavily rely on fixed expert rules, leading to low …

Smart Contract Vulnerability Detection: From Pure Neural Network to Interpretable Graph Feature and Expert Pattern Fusion

2021 · arXiv (Cornell University)

Smart contracts hold digital coins worth billions of dollars, their security issues have drawn extensive attention in the past years. Towards smart contract vulnerability detection, conventional methods heavily rely on fixed expert rules, leading to …

Contrastive Learning for Cold-Start Recommendation

2021 · arXiv (Cornell University)

Recommending cold-start items is a long-standing and fundamental challenge in recommender systems. Without any historical interaction on cold-start items, CF scheme fails to use collaborative signals to infer user preference on these items. To solve …

A New Radar Signal Multiparameter-Based Deinterleaving Method

2022 · arXiv (Cornell University)

Radar signal deinterleaving has been extensively and thoroughly investigated in the electronic reconnaissance field. In this work, a new radar signal multiparameter-based deinterleaving method is proposed. In this method, semantic information composed of the pulse …

Provably Learning Diverse Features in Multi-View Data with Midpoint Mixup

2022 · arXiv (Cornell University)

Mixup is a data augmentation technique that relies on training using random convex combinations of data points and their labels. In recent years, Mixup has become a standard primitive used in the training of state-of-the-art …

MultiCBR: Multi-view Contrastive Learning for Bundle Recommendation

2023 · arXiv (Cornell University)

Bundle recommendation seeks to recommend a bundle of related items to users to improve both user experience and the profits of platform. Existing bundle recommendation models have progressed from capturing only user-bundle interactions to the …

Reference-frame-independent quantum key distribution with two-way classical communication

2024 · Chinese Physics B

Abstract The data post-processing scheme based on two-way classical communication (TWCC) can improve the tolerable bit error rate and extend the maximal transmission distance when used in a quantum key distribution (QKD) system. In this …

Automatic modulation classification method combining multimodal information and deep learning

2024

In this paper, we propose a data-driven automatic modulation classification (AMC) algorithm for cross-domain modulation classification scenarios that involve variations in signal symbol rate between the source domain and the target domain. In real-world situations, …

Research and application of key technologies for large-scale time-series data collection and sharing in power grid WAMS

2024 · IET conference proceedings.

With the acceleration of the construction of new power systems and the continuous increase in the proportion of new energy generation, the demand for data sharing and application collaboration among various levels of dispatch master …

Reinforced Lifelong Editing for Language Models

2025 · arXiv (Cornell University)

Large language models (LLMs) acquire information from pre-training corpora, but their stored knowledge can become inaccurate or outdated over time. Model editing addresses this challenge by modifying model parameters without retraining, and prevalent approaches leverage …

Optimization of Intermittent Sampling and Repeating Jamming Waveform Based on DPSO

2024

Intermittent sampling and repeating jamming is an effective jamming method against linear frequency modulation pulse radar, but it suffers from issues such as strong regularity in the distribution of false targets and lag of primary …

Enhancing Temporal Sensitivity of Large Language Model for Recommendation with Counterfactual Tuning

2025 · arXiv (Cornell University)

Recent advances have applied large language models (LLMs) to sequential recommendation, leveraging their pre-training knowledge and reasoning capabilities to provide more personalized user experiences. However, existing LLM-based methods fail to sufficiently leverage the rich temporal …

TEM

2018

While collaborative filtering is the dominant technique in personalized recommendation, it models user-item interactions only and cannot provide concrete reasons for a recommendation. Meanwhile, the rich side information affiliated with user-item interactions (e.g., user demographics …

Explainable Reasoning over Knowledge Graphs for Recommendation

2018 · arXiv (Cornell University)

Incorporating knowledge graph into recommender systems has attracted increasing attention in recent years. By exploring the interlinks within a knowledge graph, the connectivity between users and items can be discovered as paths, which provide rich …

Deep Item-based Collaborative Filtering for Top-N Recommendation

2019 · ACM Transactions on Information Systems

Item-based Collaborative Filtering (ICF) has been widely adopted in recommender systems in industry, owing to its strength in user interest modeling and ease in online personalization. By constructing a user’s profile with the items that …

Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences

2019

Incorporating knowledge graph (KG) into recommender system is promising in improving the recommendation accuracy and explainability. However, existing methods largely assume that a KG is complete and simply transfer the ”knowledge” in KG at the …

KGAT

2019

To provide more accurate, diverse, and explainable recommendation, it is compulsory to go beyond modeling user-item interactions and take side information into account. Traditional methods like factorization machine (FM) cast it as a supervised learning …

Neural Graph Collaborative Filtering

2019

Learning vector representations (aka. embeddings) of users and items lies at the core of modern recommender systems. Ranging from early matrix factorization to recently emerged deep learning based methods, existing efforts typically obtain a user's …

Outer Product-based Neural Collaborative Filtering

2018

In this work, we contribute a new multi-layer neural network architecture named ONCF to perform collaborative filtering. The idea is to use an outer product to explicitly model the pairwise correlations between the dimensions of …

MMGCN

2019

Personalized recommendation plays a central role in many online content sharing platforms. To provide quality micro-video recommendation service, it is of crucial importance to consider the interactions between users and items (i.e. micro-videos) as well …

LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation

2020 · arXiv (Cornell University)

Graph Convolution Network (GCN) has become new state-of-the-art for collaborative filtering. Nevertheless, the reasons of its effectiveness for recommendation are not well understood. Existing work that adapts GCN to recommendation lacks thorough ablation analyses on …

Disentangled Graph Collaborative Filtering

2020

Learning informative representations of users and items from the interaction data is of crucial importance to collaborative filtering (CF). Present embedding functions exploit user-item relationships to enrich the representations, evolving from a single user-item instance …

LightGCN

2020

Graph Convolution Network (GCN) has become new state-of-the-art for collaborative filtering. Nevertheless, the reasons of its effectiveness for recommendation are not well understood. Existing work that adapts GCN to recommendation lacks thorough ablation analyses on …

Bias and Debias in Recommender System: A Survey and Future Directions

2022 · ACM Transactions on Information Systems

While recent years have witnessed a rapid growth of research papers on recommender system (RS) , most of the papers focus on inventing machine learning models to better fit user behavior data. However, user behavior …

Self-supervised Graph Learning for Recommendation

2021

Representation learning on user-item graph for recommendation has evolved from using single ID or interaction history to exploiting higher-order neighbors. This leads to the success of graph convolution networks (GCNs) for recommendation such as PinSage …

Reinforced negative sampling over knowledge graph for recommendation

2020 · Singapore Management University Institutional Knowledge (InK) (Singapore Management University)

National Research Foundation (NRF) Singapore under International Research Centre in Singapore Funding Initiative

Learning Intents behind Interactions with Knowledge Graph for Recommendation

2021

Knowledge graph (KG) plays an increasingly important role in recommender systems. A recent technical trend is to develop end-to-end models founded on graph neural networks (GNNs). However, existing GNN-based models are coarse-grained in relational modeling, …

A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation

2022 · IEEE Transactions on Knowledge and Data Engineering

Influenced by the great success of deep learning in computer vision and language understanding, research in recommendation has shifted to inventing new recommender models based on neural networks. In recent years, we have witnessed significant …

Graph Neural Networks for Recommender System

2022 · Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

Recently, graph neural network (GNN) has become the new state-of-the-art approach in many recommendation problems, with its strong ability to handle structured data and to explore high-order information. However, as the recommendation tasks are diverse …