VLoRA: Adapting Vision-Language Models for Multimodal Recommendation via Two-Stage Low-Rank Adaptation

Learning item representations that unify content semantics and collaborative filtering (CF) signals is crucial for sequential recommendation. While recent large language model-based methods have shown promise, they often ignore visual information that is important for user decisions. Vision-language models (VLMs) provide a natural foundation for multimodal recommendation, but directly fine-tuning them poses three challenges: objective interference between CF alignment and embedding learning, modality-length asymmetry, and representation collapse under sparse supervision. We propose VLoRA, a parameter-efficient two-stage post-training framework that adapts a frozen decoder-only VLM into a CF-aware multimodal embedding model. In the first stage, collaborative signals are injected through autoregressive next-item prediction over user interaction sequences. In the second stage, item embeddings are refined using bidirectional attention and three complementary objectives: Multimodal Masked Next Token Prediction for modality-asymmetric masking, dual-view self-contrastive learning for discriminative embeddings, and representation-preservation regularization to prevent representation collapse. With progressive LoRA unfreezing, VLoRA introduces trainable parameters equivalent to only 0.4% of the backbone. Experiments show that VLoRA consistently outperforms twelve strong baselines in both in-domain and out-of-domain settings, achieving an average improvement of 4.4% on NDCG@10 across all evaluation settings. Further analyses verify the effectiveness and generalizability of the proposed framework.