Personalized Academic Information Retrieval Using User Profiles and Large Language Model Agents
Keywords:
personalized information retrieval; large language models; user profiling; retrieval-augmented generation; academic search; algorithmic governance; scholarly infrastructureAbstract
Academic information retrieval has long struggled with a fundamental tension between generic relevance and individual scholarly context. The rapid growth of digital libraries, preprint repositories, and interdisciplinary publication venues has intensified this tension by fragmenting the search landscape and increasing the ambiguity of short queries. This paper examines the role of large language model agents in enabling personalized academic information retrieval through the construction, maintenance, and operational use of rich user profiles. Rather than treating personalization as a lightweight ranking adjustment, the paper argues that effective academic search requires a system-level integration of explicit and implicit user signals, contextual query understanding, retrieval orchestration, explanation generation, and long-term memory. We analyze architectural configurations in which large language model agents act as mediators between user profiles and heterogeneous retrieval backends, including sparse lexical indexes, dense vector stores, and external academic databases. The discussion emphasizes structural trade-offs related to latency, provenance, interpretability, privacy, fairness, and sustainability. We also consider governance challenges that arise when user profiles are inferred from reading behavior, writing trajectories, and citation practices. The paper contributes a conceptual framework for understanding personalized academic retrieval as a socio-technical infrastructure problem rather than a purely algorithmic optimization task. It concludes with directions for robust evaluation, policy-aware design, and sustainable deployment in university libraries, research institutions, and scholarly search platforms.
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