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

Social Proof

Social proof is a form of persuasion based on comparison and conformity. People compare their behavior and actions to what others are doing and will change to conform to do what their peers do.

Social Exchange Theory

Social Exchange Theory

We have discussed why we form relationships, what attracts us to others, and different types of love. But what determines whether we are satisfied with and stay in a relationship? One theory that provides an explanation is social exchange theory. According to social exchange theory, we act as naïve economists in keeping a tally of the ratio of costs and benefits of forming and maintaining a relationship with others (Rusbult & Van Lange, 2003).

Ogive Graph

Ogive Graph

An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...

Multiple Bar Graph

Multiple Bar Graph

As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...

Social Facilitation

Social Facilitation

Not all intergroup interactions lead to negative outcomes. Sometimes, being in a group situation can improve performance. Social facilitation occurs when an individual performs better when an audience is watching than when the individual performs the behavior alone. This typically occurs when people are performing a task for which they are skilled.

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...

You might also read

Related Articles

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

Sort by

Same author

The Growth-Decay Mechanism: A Universal Pathway for Cyclocarbon Fusion.

Inorganic chemistry·2026

Same author

High-resolution nanopore peptide sensing, profiling and sequence assembly.

Nature nanotechnology·2026

Same author

Unusual Anti-Thermoplastics and Low Thermal Expansion in 2D Metal Halide Crystals.

Advanced materials (Deerfield Beach, Fla.)·2026

Same author

Reconfigurable and nonvolatile photo-pyroelectricity in a ceramic-like biaxial molecular ferroelectric via polarization engineering.

Science advances·2026

Same author

Immune cells influence chronic spontaneous urticaria by inflammatory proteins: Mendelian randomization and bioinformatic analysis.

The journal of allergy and clinical immunology. Global·2026

Same author

Efficacy of executive function training in preschool children with ASD: a randomized controlled trial.

Child and adolescent psychiatry and mental health·2026

Same journal

Application of ephrin-B2 loaded glycol chitosan-silk fibroin hydrogel in the treatment of diabetic refractory wounds.

Scientific reports·2026

Same journal

International expert Delphi consensus on thromboprophylaxis in metabolic and bariatric surgery.

Scientific reports·2026

Same journal

Assessing the cross-region knowledge transfer capability of selected deep learning building vectorization methods in the context of available training datasets.

Scientific reports·2026

Same journal

Feasibility and preliminary effects of outdoor versus indoor cognitive-motor therapy in women with Alzheimer's disease: A randomized single-blind pilot study.

Scientific reports·2026

Same journal

Hallmarks of social action in the vocal turn-taking of wild common marmosets (Callithrix jacchus).

Scientific reports·2026

Same journal

Role and mechanism of AOPPs-induced NOX4-mediated ferroptosis in intervertebral disc degeneration.

Scientific reports·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 11, 2025

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

Multi-view graph contrastive learning for social recommendation.

Rui Chen¹, Jialu Chen¹, Xianghua Gan²

¹School of Business Administration, Faculty of Business Administration, Southwestern University of Finance and Economics, Chengdu, 611130, China.

Scientific Reports

|September 30, 2024

Summary

This summary is machine-generated.

This study introduces MultiCSR, a novel framework for social recommendation systems that uses multi-view contrastive learning to integrate user social networks and item knowledge graphs, improving recommendation accuracy.

Keywords:

Collaborative fltering Contrastive Learning Graph neural network Recommendation

More Related Videos

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

Published on: April 11, 2025

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Related Experiment Videos

Last Updated: Jun 11, 2025

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

Published on: April 11, 2025

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Area of Science:

Computer Science
Artificial Intelligence
Data Science

Background:

Online social media's growth necessitates advanced recommendation systems.
Traditional systems struggle with data sparsity and understanding user preferences.
Leveraging social connections and item semantics is key to improving recommendations.

Purpose of the Study:

To propose a novel Multi-view Contrastive learning framework for Social Recommendation (MultiCSR).
To adaptively incorporate user social networks and item knowledge graphs into recommendation models.
To enhance user preference modeling by aligning different data views.

Main Methods:

Developed a Multi-view Contrastive learning framework (MultiCSR).
Integrated user social networks and item knowledge graphs.
Employed a multi-view contrastive learning process for information alignment and mutual enhancement.

Main Results:

MultiCSR demonstrated superior performance over existing recommendation methods.
Experiments on three real-world datasets validated the framework's effectiveness.
Ablation studies provided insights into the framework's underlying mechanisms.

Conclusions:

The proposed MultiCSR framework effectively enhances social recommendation systems.
Integrating social networks and item knowledge graphs via multi-view contrastive learning improves recommendation quality.
The framework offers a robust approach to mitigating data sparsity and understanding user preferences.