Search Results for author:
Found 28 papers, 10 papers with code
Generating Reliable Friends via Adversarial Training to Improve Social Recommendation
Most of the recent studies of social recommendation assume that people share similar preferences with their friends and the online social relations are helpful in improving traditional recommender systems.
Recommender Systems Based on Generative Adversarial Networks: A Problem-Driven Perspective
In general, two lines of research have been conducted, and their common ideas can be summarized as follows: (1) for the data noise issue, adversarial perturbations and adversarial sampling-based training often serve as a solution; (2) for the data sparsity issue, data augmentation--implemented by capturing the distribution of real data under the minimax framework--is the primary coping strategy.
Enhancing Social Recommendation with Adversarial Graph Convolutional Networks
Social recommender systems are expected to improve recommendation quality by incorporating social information when there is little user-item interaction data.
Reconstruction of high-resolution 6x6-mm OCT angiograms using deep learning
The reconstructed 6x6-mm angiograms have significantly lower noise intensity and better vascular connectivity than the original images.
Path-Based Reasoning over Heterogeneous Networks for Recommendation via Bidirectional Modeling
Despite their effectiveness, these models are often confronted with the following limitations: (1) Most prior path-based reasoning models only consider the influence of the predecessors on the subsequent nodes when modeling the sequences, and ignore the reciprocity between the nodes in a path; (2) The weights of nodes in the same path instance are usually assumed to be constant, whereas varied weights of nodes can bring more flexibility and lead to expressive modeling; (3) User-item interactions are noisy, but they are often indiscriminately exploited.
MLBF-Net: A Multi-Lead-Branch Fusion Network for Multi-Class Arrhythmia Classification Using 12-Lead ECG
MLBF-Net is composed of three components: 1) multiple lead-specific branches for learning the diversity of multi-lead ECG; 2) cross-lead features fusion by concatenating the output feature maps of all branches for learning the integrity of multi-lead ECG; 3) multi-loss co-optimization for all the individual branches and the concatenated network.
Disinformation in the Online Information Ecosystem: Detection, Mitigation and Challenges
We then proceed to discuss the inherent challenges in disinformation research, and then elaborate on the computational and interdisciplinary approaches towards mitigation of disinformation, after a short overview of the various directions explored in detection efforts.
Multiple-element joint detection for Aspect-Based Sentiment Analysis
In this paper, we propose a novel end-to-end multiple-element joint detection model (MEJD), which effectively extracts all (target, aspect, sentiment) triples from a sentence.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
Skeleton-based Approaches based on Machine Vision: A Survey
Recently, skeleton-based approaches have achieved rapid progress on the basis of great success in skeleton representation.
BridgeDPI: A Novel Graph Neural Network for Predicting Drug-Protein Interactions
Therefore, we hope to develop a novel supervised learning method to learn the PPAs and DDAs effectively and thereby improve the prediction performance of the specific task of DPI.
Socially-Aware Self-Supervised Tri-Training for Recommendation
Under this scheme, only a bijective mapping is built between nodes in two different views, which means that the self-supervision signals from other nodes are being neglected.
Ready for Emerging Threats to Recommender Systems? A Graph Convolution-based Generative Shilling Attack
To explore the robustness of recommender systems, researchers have proposed various shilling attack models and analyzed their adverse effects.
Double-Scale Self-Supervised Hypergraph Learning for Group Recommendation
Technically, for (1), a hierarchical hypergraph convolutional network based on the user- and group-level hypergraphs is developed to model the complex tuplewise correlations among users within and beyond groups.
Who Are the Best Adopters? User Selection Model for Free Trial Item Promotion
By providing users with opportunities to experience goods without charge, a free trial makes adopters know more about products and thus encourages their willingness to buy.
Predictive and Contrastive: Dual-Auxiliary Learning for Recommendation
To explore recommendation-specific auxiliary tasks, we first quantitatively analyze the heterogeneous interaction data and find a strong positive correlation between the interactions and the number of user-item paths induced by meta-paths.
Shilling Black-box Recommender Systems by Learning to Generate Fake User Profiles
Leg-UP learns user behavior patterns from real users in the sampled ``templates'' and constructs fake user profiles.
Efficient Bi-Level Optimization for Recommendation Denoising
To efficiently solve this bi-level optimization problem, we employ a weight generator to avoid the storage of weights and a one-step gradient-matching-based loss to significantly reduce computational time.
Debiasing Recommendation by Learning Identifiable Latent Confounders
Confounding bias arises due to the presence of unmeasured variables (e. g., the socio-economic status of a user) that can affect both a user's exposure and feedback.
Perfused and Nonperfused Microaneurysms Identified and Characterized by Structural and Angiographic OCT
Although not all MAs seen with FA were identified with OCT, some MAs seen with OCT were not visible with FA or FP.
Emulating Reader Behaviors for Fake News Detection
To this end, we propose an approach of Emulating the behaviors of readers (Ember) for fake news detection on social media, incorporating readers' reading and verificating process to model news from the component perspective thoroughly.
Reformulating Sequential Recommendation: Learning Dynamic User Interest with Content-enriched Language Modeling
Recommender systems are indispensable in the realm of online applications, and sequential recommendation has enjoyed considerable prevalence due to its capacity to encapsulate the dynamic shifts in user interests.
Poisoning Attacks Against Contrastive Recommender Systems
Contrastive learning (CL) has recently gained significant popularity in the field of recommendation.
Poisoning Attacks against Recommender Systems: A Survey
This survey paper provides a systematic and up-to-date review of the research landscape on Poisoning Attacks against Recommendation (PAR).
MCRPL: A Pretrain, Prompt & Fine-tune Paradigm for Non-overlapping Many-to-one Cross-domain Recommendation
To overcome the above challenges, we focus on NMCR, and devise MCRPL as our solution.
Entire Chain Uplift Modeling with Context-Enhanced Learning for Intelligent Marketing
ECUP consists of two primary components: 1) the Entire Chain-Enhanced Network, which utilizes user behavior patterns to estimate ITE throughout the entire chain space, models the various impacts of treatments on each task, and integrates task prior information to enhance context awareness across all stages, capturing the impact of treatment on different tasks, and 2) the Treatment-Enhanced Network, which facilitates fine-grained treatment modeling through bit-level feature interactions, thereby enabling adaptive feature adjustment.
Survival Prediction Across Diverse Cancer Types Using Neural Networks
This study introduces a pioneering approach to enhance survival prediction models for gastric and Colon adenocarcinoma patients.
Convolutional neural network classification of cancer cytopathology images: taking breast cancer as an example
To address the challenges of dependence on pathologists expertise and the time-consuming nature of achieving accurate breast pathological image classification, this paper introduces an approach utilizing convolutional neural networks (CNNs) for the rapid categorization of pathological images, aiming to enhance the efficiency of breast pathological image detection.
Breast Cancer Image Classification Method Based on Deep Transfer Learning
To address the issues of limited samples, time-consuming feature design, and low accuracy in detection and classification of breast cancer pathological images, a breast cancer image classification model algorithm combining deep learning and transfer learning is proposed.
