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Related Experiment Video

Updated: Jul 1, 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

A switching-based deep learning framework for personalized and adaptive E-commerce recommendations.

Kapil Saini1, Ajmer Singh2, Manoj Diwakar3,4

  • 1School of Computer Science and Engineering, Geeta University, Panipat, Haryana, 132145, India.

Scientific Reports
|February 24, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel switching-based hybrid recommendation system to effectively handle diverse user profiles and cross-domain recommendations. The system significantly improves recommendation accuracy and performance across various user segments, addressing challenges like data sparsity and real-time adaptability.

Keywords:
Cross-domain recommendationsDiverse user profilesMulti-task learningRecommendation systemSwitching-based hybrid models

Related Experiment Videos

Last Updated: Jul 1, 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

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Recommendation systems face challenges with diverse user profiles, data sparsity, and the cold-start problem.
  • Existing systems struggle with cross-domain recommendations and adapting to real-time user preference shifts.
  • Balancing personalization and recommendation diversity is crucial for user satisfaction.

Purpose of the Study:

  • To propose an innovative switching-based hybrid recommendation system to cater to diverse user profiles.
  • To address challenges in cross-domain recommendations and real-time adaptability.
  • To improve recommendation precision and efficiency for varied user interaction histories and preferences.

Main Methods:

  • Categorized users into three groups: newbies, light users, and heavy users.
  • Developed a switching-based hybrid recommendation system architecture.
  • Optimized multiple objectives using signals like product views for enhanced performance.

Main Results:

  • Reduced validation loss from 0.3414 to 0.1545 for new users (54.7% reduction).
  • Improved Hit Rate@10 (HR@10) from 0.30 to 0.60 (100% increase) and NDCG@10 from 0.35 to 0.65 (85.7% increase) for light users.
  • Achieved high predictive performance for implicit feedback: 0.91 accuracy, 0.89 precision, 0.88 recall, and 0.89 F1 score.

Conclusions:

  • The proposed system effectively accommodates diverse user profiles and improves recommendation quality.
  • Switching-based hybrid models offer a promising solution for cross-domain and adaptive recommendations.
  • The system demonstrates significant performance gains over baseline methods across different user segments.