NLP-Driven Intelligent Recommendation System: Innovation and Practice

Dear Colleagues,

Recent advancements in artificial intelligence (AI), particularly in natural language processing (NLP), large language models (LLMs), transformer architectures, and retrieval-augmented generation (RAG) that are transforming the landscape of intelligent recommendation systems. These technologies enable highly personalized, context-aware, and explainable recommendations by leveraging multimodal data and interactive agents.

This Special Issue seeks high-quality original research, comprehensive reviews, and case studies that explore the innovation and practical implementation of next-generation recommendation systems driven by NLP and related AI techniques. We encourage submissions addressing system architectures, deployment strategies, evaluation frameworks, and pressing challenges such as interpretability, fairness, scalability, and privacy.

Topics of interest include, but are not limited to the following:

• NLP and LLM-powered recommendation systems;
• Transformer architecture and retrieval-augmented generation (RAG);
• Generative models and reinforcement learning for personalization;
• Autonomous and multi-agent systems for adaptive recommendations;
• Conversational and interactive recommendation platforms;
• Multimodal data fusion (text, images, audio, video) for enriched user modelling;
• Explainability, transparency, fairness, and ethics in recommendation outcomes;
• Cold-start problems and data-efficient learning methods;
• Knowledge graphs and semantic reasoning for context-aware systems;
• Scalability, robustness, and real-time deployment of NLP-driven systems;
• Privacy-preserving and trustworthy recommendation frameworks;
• Evaluation metrics, benchmarking standards, and reproducibility in recommendation research;
• Human-centred AI and user modelling for personalized experiences.

Dr. Yasir Hussain
Prof. Dr. Yu Zhou
Guest Editors

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