A social information sensitive model for conversational recommender systems

A social information sensitive model for conversational recommender systems

Conversational recommender systems (CRS) facilitate natural language interactions for more effective item suggestions. While these systems show promise, they face challenges in effectively utilizing and integrating informative data with conversation history through semantic fusion. In this study we...

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Bibliographic Details
Main Authors: Abdulaziz Mohammed, Mingwei Zhang, Gehad Abdullah Amran, Husam M. Alawadh, Ruizhe Wang, Amerah Alabrah, Ali A. Al-Bakhrani
Format: Article
Language:English
Published: PeerJ Inc. 2025-08-01
Series:PeerJ Computer Science
Subjects:
Conversational recommendation system
Contrastive learning
Semantic fusion
Social recommendation
NLP
Online Access:https://peerj.com/articles/cs-3067.pdf
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https://peerj.com/articles/cs-3067.pdf

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