Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Observational Learning

Observational Learning

Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...

Associative Learning

Associative Learning

Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...

Cognitive Learning

Cognitive Learning

Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...

Causes of Similarity-Dissimilarity Effect

Causes of Similarity-Dissimilarity Effect

The similarity-dissimilarity effect, a fundamental concept in social psychology, explains how interpersonal similarities and differences influence attraction and social interactions. This effect is supported by three key psychological perspectives: balance theory, social comparison theory, and consensual validation.Balance Theory and Cognitive ConsistencyBalance theory, developed by Fritz Heider, posits that individuals seek cognitive consistency in their relationships. When two people share...

Correspondence Bias

Correspondence Bias

Correspondence bias, also referred to as the fundamental attribution error, describes the tendency to attribute another person’s behavior to internal characteristics rather than situational influences. This cognitive bias leads individuals to overlook external factors that may be influencing actions, thereby fostering potentially inaccurate assessments of others’ intentions and dispositions.Empirical Evidence for Correspondence BiasResearch has consistently demonstrated the...

Introduction to Learning

Introduction to Learning

Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Iridocorneal endothelial syndrome manifesting 4.5 years after toric implantable collamer lens implantation: a case report.

BMC ophthalmology·2026

Same author

Bioinspired Carbon Radical Catalysis.

Journal of the American Chemical Society·2026

Same author

DHCR7 promotes granulosa cells ferroptosis and represents a potential therapeutic target in premature ovarian insufficiency.

Biochimica et biophysica acta. Molecular cell research·2026

Same author

Proteomic and transcriptomic analyses identify the role of RBM25 in the malignant progression of glioma.

The International journal of neuroscience·2026

Same author

Association of gut microbiota and inflammatory markers with enteral nutrition intolerance in patients with early-stage moderate-to-severe intracerebral hemorrhage.

Microbiology spectrum·2026

Same author

Multi-Stage State Assessment of Breakers Based on TCWGAN-GP and XGBoost Under Insufficient Samples.

Sensors (Basel, Switzerland)·2026

Search research articles

Related Experiment Video

Updated: Jan 15, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Contrastive learning on high-order noisy graphs for collaborative recommendation.

Jiahao Wang¹, Qingshuai Wang¹, Noor Farizah Ibrahim²

¹School of Computer Sciences, Universiti Sains Malaysia, 11800, Penang, Malaysia.

Scientific Reports

|October 6, 2025

Summary

This summary is machine-generated.

This study introduces RHO-GCL, a novel framework for recommendation systems that enhances graph-based collaborative filtering by capturing higher-order user-item relations and improving robustness against noisy data. It significantly boosts performance in sparse scenarios.

Keywords:

Collaborative filtering Contrastive learning Graph neural networks Recommender systems

Related Experiment Videos

Last Updated: Jan 15, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

Artificial Intelligence
Machine Learning
Data Mining

Background:

Graph-based collaborative filtering is valuable for recommendation systems but struggles with data sparsity.
Existing contrastive learning methods fail to capture higher-order associations and handle noisy data effectively.

Purpose of the Study:

To propose RHO-GCL, a framework addressing limitations in graph-based collaborative filtering.
To enhance the capture of higher-order user-item associations and improve robustness against graph noise.

Main Methods:

RHO-GCL models higher-order graph structures for richer user-item relations.
It integrates noise-enhanced contrastive learning to mitigate the impact of noisy interactions.
A hierarchical perspective and noise perturbation mechanisms are employed.

Main Results:

RHO-GCL demonstrated significant performance improvements on benchmark datasets (MovieLens, Yelp).
The framework showed enhanced resistance to noise interference compared to existing models.
Systematic tests validated the effectiveness of the proposed strategies.

Conclusions:

RHO-GCL effectively enhances recommendation systems' ability to understand complex graph data.
The structural feature enhancement and noise-balancing mechanisms offer a reliable solution for real-world applications.
This approach improves collaborative filtering in sparse and noisy environments.