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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| Format: | Article |
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PeerJ Inc.
2025-08-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-3067.pdf |
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| author | Abdulaziz Mohammed Mingwei Zhang Gehad Abdullah Amran Husam M. Alawadh Ruizhe Wang Amerah Alabrah Ali A. Al-Bakhrani |
| author_facet | Abdulaziz Mohammed Mingwei Zhang Gehad Abdullah Amran Husam M. Alawadh Ruizhe Wang Amerah Alabrah Ali A. Al-Bakhrani |
| author_sort | Abdulaziz Mohammed |
| collection | DOAJ |
| description | 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 present an innovative framework for extracting social information from conversational datasets by inferring ratings and constructing user-item interaction and user-user relationship graphs. We introduce a social information sensitive semantic fusion (SISSF) method that employs contrastive learning (CL) to bridge the semantic gap between generated social information and conversation history. We evaluated the framework on two public datasets (ReDial and INSPIRED) using both automatic and human evaluation metrics. Our SISSF framework demonstrated significant improvements over baseline models across all metrics. For the ReDial dataset, SISSF achieved superior performance in recommendation tasks (R@1: 0.062, R@50: 0.437) and conversational quality metrics (Distinct-2: 4.223, Distinct-3: 5.595, Distinct-4: 6.155). Human evaluation showed marked improvement in both fluency (1.81) and informativeness (1.63). We observed similar performance gains on the INSPIRED dataset, with notable improvements in recommendation accuracy (R@1: 0.046, R@10: 0.129, R@50: 0.269) and response diversity (Distinct-2: 2.061, Distinct-3: 4.293, Distinct-4: 6.242). The experimental results consistently validate the effectiveness of our approach in both recommendation and conversational tasks. These findings suggest that incorporating social context through CL can significantly improve the personalization and relevance of recommendations in conversational systems. |
| format | Article |
| id | doaj-art-3e28e091e26a4a59a3d522ecc31bc9b4 |
| institution | Kabale University |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-3e28e091e26a4a59a3d522ecc31bc9b42025-08-23T15:05:16ZengPeerJ Inc.PeerJ Computer Science2376-59922025-08-0111e306710.7717/peerj-cs.3067A social information sensitive model for conversational recommender systemsAbdulaziz Mohammed0Mingwei Zhang1Gehad Abdullah Amran2Husam M. Alawadh3Ruizhe Wang4Amerah Alabrah5Ali A. Al-Bakhrani6College of Software Engineering, Northeastern University, Shenyang, ChinaCollege of Software Engineering, Northeastern University, Shenyang, ChinaDepartment of Management Science and Engineering, Dalian University of Technology, Dalian, ChinaDepartment of English Language, College of Language Sciences, King Saud University, Riyadh, Saudi ArabiaCollege of Software Engineering, Northeastern University, Shenyang, ChinaDepartment of Information Systems, College of Computer and Information Science, King Saud University, Riyadh, Saudi ArabiaCollege of Software Engineering, Dalian University of Technology, Dalian, ChinaConversational 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 present an innovative framework for extracting social information from conversational datasets by inferring ratings and constructing user-item interaction and user-user relationship graphs. We introduce a social information sensitive semantic fusion (SISSF) method that employs contrastive learning (CL) to bridge the semantic gap between generated social information and conversation history. We evaluated the framework on two public datasets (ReDial and INSPIRED) using both automatic and human evaluation metrics. Our SISSF framework demonstrated significant improvements over baseline models across all metrics. For the ReDial dataset, SISSF achieved superior performance in recommendation tasks (R@1: 0.062, R@50: 0.437) and conversational quality metrics (Distinct-2: 4.223, Distinct-3: 5.595, Distinct-4: 6.155). Human evaluation showed marked improvement in both fluency (1.81) and informativeness (1.63). We observed similar performance gains on the INSPIRED dataset, with notable improvements in recommendation accuracy (R@1: 0.046, R@10: 0.129, R@50: 0.269) and response diversity (Distinct-2: 2.061, Distinct-3: 4.293, Distinct-4: 6.242). The experimental results consistently validate the effectiveness of our approach in both recommendation and conversational tasks. These findings suggest that incorporating social context through CL can significantly improve the personalization and relevance of recommendations in conversational systems.https://peerj.com/articles/cs-3067.pdfConversational recommendation systemContrastive learningSemantic fusionSocial recommendationNLP |
| spellingShingle | Abdulaziz Mohammed Mingwei Zhang Gehad Abdullah Amran Husam M. Alawadh Ruizhe Wang Amerah Alabrah Ali A. Al-Bakhrani A social information sensitive model for conversational recommender systems PeerJ Computer Science Conversational recommendation system Contrastive learning Semantic fusion Social recommendation NLP |
| title | A social information sensitive model for conversational recommender systems |
| title_full | A social information sensitive model for conversational recommender systems |
| title_fullStr | A social information sensitive model for conversational recommender systems |
| title_full_unstemmed | A social information sensitive model for conversational recommender systems |
| title_short | A social information sensitive model for conversational recommender systems |
| title_sort | social information sensitive model for conversational recommender systems |
| topic | Conversational recommendation system Contrastive learning Semantic fusion Social recommendation NLP |
| url | https://peerj.com/articles/cs-3067.pdf |
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