Explainable Personalized Retrieval Using Large Language Models and Persistent User Preference Representations
Keywords:
personalized retrieval, large language models, explainability, user modeling, retrieval-augmented generation, persistent memory, fairness, governanceAbstract
Personalized retrieval systems increasingly operate within a complex socio-technical environment in which user intent is dynamic, contextual, and subject to privacy constraints. Large language models offer new capacity to interpret natural language queries, generate structured retrieval targets, and explain ranking decisions in fluent prose. However, the integration of persistent user preference representations into retrieval pipelines raises substantial architectural, governance, and fairness challenges. This paper presents a system-level examination of explainable personalized retrieval that combines large language models with long-lived user memory structures. The analysis focuses on how persistent preference representations can be constructed, updated, audited, and deployed without sacrificing transparency or robustness. We discuss retrieval-augmented generation, embedding-based memory, user modeling, explanation generation, and the structural trade-offs among latency, accuracy, privacy, and interpretability. The paper further considers infrastructure requirements, sustainability, fairness risks, and policy implications. Rather than proposing a single algorithmic solution, the paper develops a conceptual architecture and a set of design principles for building systems that balance personalization with explainability. The discussion draws on recent advances in dense retrieval, language model interpretability, and responsible artificial intelligence. The goal is to provide a publication-ready synthesis of the key system-level concerns and forward-looking research directions for persistent user modeling in retrieval systems.
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