Sentiment-Aware and Explainable AI-Based Cross-Domain Recommendation System

Cross-Domain Recommendation Systems (CDRS) enhance traditional recommendation models by transferring knowledge across different domains, thereby improving the personalization of suggested content.The integration of Explainable AI (XAI) ensures transparency in recommendation systems, addressing concerns regarding trust and interpretability.Additionally, sentiment analysis is crucial in refining recommendations by capturing the emotions embedded in user reviews and feedback.This paper explores the concept of a sentiment-aware and explainable AI-based cross-domain recommendation system by discussing key methodologies, challenges, and potential advancements in the field.The research highlights how a hybrid recommendation model can optimize user satisfaction by balancing accuracy, interpretability, and personalized content delivery.

Publication Details

Journal
IJARCCE
Published
2025-04-07
DOI
https://doi.org/10.17148/ijarcce.2025.14385
Primary Topic
Recommender Systems and Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

Sentiment-Aware and Explainable AI-Based Cross-Domain Recommendation System

IJARCCE
Recommender Systems and Techniques
article

Sentiment-Aware and Explainable AI-Based Cross-Domain Recommendation System

article en

Abstract

Cross-Domain Recommendation Systems (CDRS) enhance traditional recommendation models by transferring knowledge across different domains, thereby improving the personalization of suggested content.The integration of Explainable AI (XAI) ensures transparency in recommendation systems, addressing concerns regarding trust and interpretability.Additionally, sentiment analysis is crucial in refining recommendations by capturing the emotions embedded in user reviews and feedback.This paper explores the concept of a sentiment-aware and explainable AI-based cross-domain recommendation system by discussing key methodologies, challenges, and potential advancements in the field.The research highlights how a hybrid recommendation model can optimize user satisfaction by balancing accuracy, interpretability, and personalized content delivery.

IJARCCEVol. 14(3)
Openalex Percentile: Top 92%
Recommender Systems and Techniques
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