Workshop on LLM & Agents for Recommendation Systems

We invite submissions exploring the intersection of Large Language Models (LLMs), intelligent agents, and recommendation systems. This workshop aims to advance the understanding of how LLM-based agents can enhance personalization, user interaction, and decision-making in recommendation systems.

Topics of Interest

We welcome research contributions on topics including, but not limited to:

• LLM-based Recommendation Agents: Design, architecture, and deployment of intelligent recommendation agents
• Multi-Agent Systems: Coordination, collaboration, and optimization in multi-agent recommendation environments
• Conversational & Interactive Recommendations: Natural language interfaces, dialogue systems, and user engagement strategies
• Evaluation & Benchmarking: Novel metrics, datasets, and evaluation frameworks for LLM-based recommenders
• Personalization & Context-Awareness: Leveraging user context, preferences, and historical data for enhanced personalization
• Explainability & Transparency: Interpretability of LLM-driven recommendations and building user trust
• Real-World Applications: Case studies and deployments in e-commerce, content platforms, healthcare, finance, and more
• Ethical & Societal Implications: Bias, fairness, privacy, and responsible AI in recommendation systems
• Hybrid Approaches: Combining LLMs with traditional recommender techniques (collaborative filtering, matrix factorization, etc.)
• Knowledge Graphs & Retrieval-Augmented Generation: Enhancing recommendations with structured knowledge and retrieval mechanisms

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Submission Guidelines