Personalized Product Search Through Joint Modeling of User Intent, Behavioral History, and Semantic Memory
Keywords:
personalized product search, joint modeling, user intent, behavioral history, semantic memory, information retrieval, system governance, large language modelsAbstract
Personalized product search operates at the intersection of information retrieval, user modeling, and large-scale machine learning infrastructure. This paper develops a systems-level analysis of joint modeling approaches that integrate three complementary streams: query-time user intent, longitudinal behavioral history, and persistent semantic memory. Rather than proposing a single ranking algorithm, the paper examines architectural trade-offs, data governance requirements, deployment patterns, and policy implications for production-scale personalized retrieval. The discussion addresses how intent representations capture immediate purchase goals, how behavioral history encodes temporal and contextual preferences, and how semantic memory supplies durable conceptual structures that stabilize personalization across sessions. Drawing on retrieval, ranking, transformer, recommender systems, privacy, and fairness literatures, the paper identifies structural tensions between real-time latency, model capacity, privacy preservation, and auditability. It further analyzes feedback loops, concept drift, cold-start conditions, and regulatory constraints. The central argument is that durable value emerges not from isolated ranking improvements but from a governed system of memory consolidation, intent inference, and policy-aware retrieval. The paper closes with a forward-looking research agenda emphasizing energy efficiency, fairness, auditability, and cross-domain transfer.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
