International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 04 | Apr 2025
p-ISSN: 2395-0072
www.irjet.net
Privacy-Preserving Large Language Model-Based Recommendation Systems - Challenges, Techniques, and Opportunities Ying Li1 1Meta Platform Inc., USA
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Abstract - As large language models (LLMs) increasingly
2. LLM-POWERED RECOMMENDER ARCHITECTURES
serve as the backbone for intelligent recommendation systems, they offer unprecedented personalization capabilities through deep contextual understanding and natural language generation. However, this advancement also raises critical privacy risks, including memorization of sensitive data, prompt injection attacks, and cross-domain inference vulnerabilities. In this survey, we present a comprehensive review of privacy-preserving techniques for LLM-based recommendation systems. We outline various LLM-powered architectures, categorize the emerging threat landscape, and analyze mitigation strategies including multi-agent designs, federated learning, differential privacy, and pseudonymization. We also identify ongoing challenges such as limited domain-specific benchmarks, inadequate user control, and fairness-privacy trade-offs. Finally, we highlight future research directions, including adaptive privacy interfaces, explainability under privacy constraints, and unified frameworks for legal compliance and ethical alignment. Our goal is to establish a foundational understanding of privacy-preserving mechanisms in the context of LLM-driven recommendation systems and to inspire further innovations that balance personalization with responsible data stewardship.
2.1 LLM as a Re-Ranker or Explainer LLMs are now frequently used to refine the outputs of traditional recommendation engines by re-ranking results based on natural language input or by providing detailed explanations for recommendations. For instance, a collaborative filtering algorithm may initially generate a list of product suggestions, but an LLM can refine this list using a nuanced understanding of a query like “comfortable shoes for all-day city walking.” In addition to enhancing relevance, LLMs can generate natural-sounding explanations—e.g., “This book matches your interest in character-driven mysteries”— which builds user trust and improves system transparency without changing the core algorithmic mechanisms.
2.2 LLM as End-to-End Recommender This architecture treats the recommendation task as one of text generation. Rather than selecting from a fixed set of options, the system generates recommendations directly in natural language. For example, a travel platform might ask an LLM to propose a complete itinerary based on a user’s preferences and constraints. These systems typically use prompt engineering and in-context learning to tailor results, which offers much more flexibility in capturing complex or evolving user preferences than traditional embedding-based methods.
Key Words: Large Language Models, Recommendation Systems, Privacy-Preserving AI, Differential Privacy, Federated Learning, Contextual Integrity, Explainability
1.INTRODUCTION
2.3 LLM for User Preference Modeling
The integration of large language models (LLMs) into recommender systems has significantly transformed how personalized digital experiences are delivered. These models enhance the quality of recommendations by enabling more sophisticated natural language understanding, richer contextual reasoning, and more interactive user engagement. However, their adoption has also introduced a range of new privacy concerns that traditional recommendation frameworks were not designed to handle. This survey provides a comprehensive exploration of the current landscape of privacy-preserving techniques within LLMpowered recommendation systems. We outline a new taxonomy of privacy risks, examine the mitigation strategies being developed to address them, and analyze the architectural patterns emerging in this space. The goal is to offer a clear roadmap for future research at the convergence of LLMs, personalization, and privacy.
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Impact Factor value: 8.315
LLMs can dynamically construct user profiles by interpreting natural language inputs such as conversation snippets, reviews, or queries. Unlike traditional systems that depend on structured logs (e.g., clicks or purchase history), LLMs can infer preferences from casual, unstructured language. For example, an offhand remark about dietary restrictions might be detected and incorporated into food recommendations. Additionally, LLMs show promise in transferring knowledge across domains—for example, learning a user’s aesthetic taste in clothing and applying it to home decor suggestions.
2.4 Agentic and Multi-Modal Systems Some of the most forward-looking systems rely on LLMs functioning as agents that actively engage with users and external systems. These systems are capable of initiating
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