Lizhen Cui

An Effective Hybrid Fraud Detection Method

2015 · Lecture notes in computer science

The rapid growth of data makes it possible for us to study human behavior patterns. Knowing the patterns of human behavior is of great use to help us detect the unusual fraud human behavior. Existing …

An Algorithm for Finding the Minimum Cost of Storing and Regenerating Datasets in Multiple Clouds

2015 · IEEE Transactions on Cloud Computing

The proliferation of cloud computing allows users to flexibly store, re-compute or transfer large generated datasets with multiple cloud service providers. However, due to the pay-as-you-go model, the total cost of using cloud services depends …

A Privacy Protection Mechanism for NoSql Database Based on Data Chunks

2016

In SaaS (Software as a Service) applications, the tenants upload their data into the databases of the cloud service provider. Because the data is not directly managed by the tenants, the security of the private …

Enhancing Collaborative Filtering with Generative Augmentation

2019

Collaborative filtering (CF) has become one of the most popular and widely used methods in recommender systems, but its performance degrades sharply for users with rare interaction data. Most existing hybrid CF methods try to …

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. …

Multi-Aspect Explainable Inductive Relation Prediction by Sentence Transformer

2023 · arXiv (Cornell University)

Recent studies on knowledge graphs (KGs) show that path-based methods empowered by pre-trained language models perform well in the provision of inductive and explainable relation predictions. In this paper, we introduce the concepts of relation …

Logic Event Graph Enhanced Narrative Generation

2023

Narrative generation aims at producing fluent long texts from input data, which is widely used in event prediction, story generation and other fields. Previous work mainly relied on text data, picture data, knowledge graphs, etc. …

Decentralized Collaborative Learning with Adaptive Reference Data for On-Device POI Recommendation

2024

In Location-based Social Networks (LBSNs), Point-of-Interest (POI) recommendation helps users discover interesting places. There is a trend to move from the conventional cloud-based model to on-device recommendations for privacy protection and reduced server reliance. Due …

Multi-modal Food Recommendation using Clustering and Self-supervised Learning

2024 · arXiv (Cornell University)

Food recommendation systems serve as pivotal components in the realm of digital lifestyle services, designed to assist users in discovering recipes and food items that resonate with their unique dietary predilections. Typically, multi-modal descriptions offer …

Knowledge Distillation Based Recommendation Systems: A Comprehensive Survey

2025 · Electronics

Deep learning-driven deep recommendation systems have achieved remarkable success in recent years. However, the deployment of deep recommendation models on resource-constrained equipment and systems (e.g., mobile devices and embedded systems) is a significant challenge. To …

Enhancing Social Recommendation With Adversarial Graph Convolutional Networks

2020 · IEEE Transactions on Knowledge and Data Engineering

Social recommender systems are expected to improve recommendation quality by incorporating social information when there is little user-item interaction data. However, recent reports from industry show that social recommender systems consistently fail in practice. According …

Are Graph Augmentations Necessary?

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

Contrastive learning (CL) recently has spurred a fruitful line of research in the field of recommendation, since its ability to extract self-supervised signals from the raw data is well-aligned with recommender systems' needs for tackling …