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Related Concept Videos

Response Surface Methodology01:16

Response Surface Methodology

854
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
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Stereotype Content Model02:16

Stereotype Content Model

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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Related Experiment Video

Updated: Apr 6, 2026

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models
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Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

Published on: December 23, 2025

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A Dynamic Recommender System for Improved Web Usage Mining and CRM Using Swarm Intelligence.

Anna Alphy1, S Prabakaran1

  • 1Department of Computer Science and Engineering, SRM University, Chennai 603203, India.

Thescientificworldjournal
|August 1, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a novel dynamic recommender system, WebBluegillRecom-annealing, inspired by fish behavior. It effectively addresses information overload and user behavior dynamics for personalized websites, outperforming traditional methods.

Related Experiment Videos

Last Updated: Apr 6, 2026

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models
07:14

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

Published on: December 23, 2025

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Area of Science:

  • Web usage mining
  • Recommender systems
  • Swarm intelligence

Background:

  • Personalizing e-business websites requires understanding user interests and behavior.
  • Online usage data presents challenges of information overload and dynamic user behavior.

Purpose of the Study:

  • To propose a dynamic recommender system, WebBluegillRecom-annealing, to address challenges in personalized web experiences.
  • To enhance website customization through dynamic recommendations.

Main Methods:

  • Utilized web usage mining techniques and software agents.
  • Incorporated swarm intelligence inspired by bluegill fish foraging behavior.
  • Developed a dynamic recommender system for handling user behavior dynamics.

Main Results:

  • The proposed system demonstrated higher precision, coverage, and F1 measure compared to traditional collaborative filtering.
  • WebBluegillRecom-annealing showed superior scalability.
  • Recommendations provided by the system mitigated overspecialization by incorporating variety.

Conclusions:

  • The WebBluegillRecom-annealing system effectively overcomes information overload and dynamic user behavior.
  • The novel approach offers significant improvements over existing collaborative filtering methods for personalized web experiences.