Exploring Multimodal Large Language Models for Next-Generation Recommendation Systems

Multimodal Large Language Models (MLLMs) integrate diverse data modalities—including textual descriptions, visual content, and contextual signals—into a unified framework for advanced machine learning tasks. In recommendation systems, these models offer a more comprehensive approach by combining user behavioral data, product metadata, and visual features to enhance relevance prediction. This research explores an end-to-end integration of MLLMs into recommendation pipelines, spanning from data preparation and model adaptation in the batch training phase to real-time serving for low latency inference. A modular architecture is introduced, built on a pre-trained transformer backbone with modality-specific encoders, allowing seamless fusion of multimodal inputs. Empirical evaluations on an e-commerce dataset reveal that the proposed MLLM-based recommender outperforms unimodal baselines, leading to higher recall and improved user satisfaction. Critical considerations for data alignment, scalability, and interpretability in real-world deployment are also discussed. These findings highlight the transformative potential of multimodal learning in next-generation recommendation systems.