Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Reinforcement01:23

Reinforcement

204
Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
204
Observational Learning01:12

Observational Learning

170
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...
170
Social Proof00:52

Social Proof

27.7K
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.
27.7K
Reinforcement Schedules01:24

Reinforcement Schedules

144
Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
144
Social Exchange Theory02:06

Social Exchange Theory

34.5K
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).
34.5K
Associative Learning01:27

Associative Learning

353
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...
353

You might also read

Related Articles

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

Sort by
Same author

Brain age gap estimation using attention-based ResNet method for Alzheimer's disease detection.

Brain informatics·2024
See all related articles

Related Experiment Video

Updated: Jun 29, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

3.7K

A social image recommendation system based on deep reinforcement learning.

Somaye Ahmadkhani1, Mohsen Ebrahimi Moghaddam1

  • 1Shahid Beheshti University, Faculty of Computer Science and Engineering, Tehran, Iran.

Plos One
|April 4, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a dynamic image recommender system using deep reinforcement learning (DRL) and novel features like emotion and style. The system significantly improves personalized image recommendations by adapting to user preferences.

More Related Videos

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.4K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.2K

Related Experiment Videos

Last Updated: Jun 29, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

3.7K
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.4K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.2K

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Information Retrieval

Background:

  • Information overload is a growing problem due to the vast amount of dynamic online content.
  • Recommender systems help mitigate information overload by analyzing user behavior to predict interests.
  • Existing social image recommender systems often use static strategies, failing to adapt to evolving user preferences.

Purpose of the Study:

  • To develop a dynamic social image recommender system that adapts to changing user preferences.
  • To enhance personalized recommendations by incorporating novel features such as emotion, style, and personality.
  • To address the challenge of state representation in reinforcement learning for recommender systems.

Main Methods:

  • A deep reinforcement learning (DRL) framework is employed for dynamic image recommendation.
  • Novel features including user emotion, style, and personality are integrated to create a characteristic vector.
  • A new state representation method is introduced to overcome limitations in reinforcement learning state definition.

Main Results:

  • The proposed dynamic recommender system significantly improves personalized image recommendation performance.
  • Experimental results demonstrate an approximate 7%-10% increase in Recall@k and Precision@k (top 100 images) compared to related works.
  • The incorporation of novel features and state representation enhances recommendation accuracy.

Conclusions:

  • The developed dynamic image recommender system effectively addresses the limitations of static approaches.
  • The DRL framework with enhanced features provides a more personalized and adaptive recommendation experience.
  • This research offers a significant advancement in the field of personalized social image recommendation.