Long-Term Preference Learning for Personalized Media Retrieval Using Large Language Models
Keywords:
personalized media retrieval; large language models; long-term preference learning; memory systems; fairness; algorithmic governance; socio-technical infrastructureAbstract
Personalized media retrieval has traditionally relied on collaborative filtering and matrix factorization, which often treat user preferences as relatively stable vectors derived from past interactions. The rise of large language models creates new possibilities for representing and updating long-term user preferences through natural language memory, semantic query understanding, and relevance explanation. However, moving from short-term query optimization to long-term preference learning introduces structural challenges that go beyond model accuracy. This paper develops a system-level analysis of long-term preference learning for personalized media retrieval using large language models. It examines how memory modeling, temporal preference evolution, retrieval integration, and ranking interact with governance, fairness, infrastructure, and sustainability concerns. We argue that a layered architecture separating parametric knowledge, user memory, retrieval indexes, and policy controls can improve robustness and accountability. The discussion emphasizes the importance of data provenance, user control over inferred preferences, fairness audits, and the distinction between transient engagement and durable preference shifts. The paper also addresses deployment trade-offs, including centralized, federated, and local memory designs, and considers the political economy of personalization. Rather than proposing a single technical solution, the paper offers a socio-technical framework for building personalized media retrieval systems that are not only effective but also legible, contestable, and aligned with user autonomy.
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