Multimodal Large Language Models for Context-Aware Personalized Search and Recommendation
Keywords:
multimodal large language models; personalized search; recommender systems; context-aware retrieval; system architecture; privacy; fairness; sustainabilityAbstract
Multimodal large language models are reshaping personalized search and recommendation by jointly representing text, image, audio, interaction histories, and situational context. This paper examines the system-level design challenges that arise when these models move from unimodal retrieval pipelines to context-aware personalization infrastructures. We analyze architectural trade-offs among modality encoders, fusion layers, retrieval indexes, generative components, and user profiling subsystems. The discussion emphasizes structural tensions between end-to-end multimodal learning and modular, auditable system design. We explore context representation, sequential preference modeling, privacy-preserving learning, and retrieval augmentation as complementary mechanisms. Special attention is given to deployment constraints, including latency, memory, energy consumption, fairness, and regulatory compliance. The paper further addresses governance arrangements that coordinate model developers, platform operators, data stewards, and external auditors. Rather than proposing a single blueprint, the study develops a socio-technical framework for evaluating multimodal personalization systems according to performance, robustness, fairness, and sustainability criteria. The analysis is grounded in recent advances in transformer-based architectures, vision-language pretraining, sequential recommendation, instruction tuning, federated learning, and differential privacy. It argues that context-aware personalization must be treated as an infrastructure problem rather than solely a modeling problem. Institutions deploying such systems need transparent model cards, continuous auditing, energy-aware resource allocation, and enforceable consent protocols. The paper concludes with policy implications and a forward-looking research agenda for responsible multimodal personalization at scale.
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