  Improving user experience and providing personalized search results in
E-commerce platforms heavily rely on understanding purchase intention. However,
existing methods for acquiring large-scale intentions bank on distilling large
language models with human annotation for verification. Such an approach tends
to generate product-centric intentions, overlook valuable visual information
from product images, and incurs high costs for scalability. To address these
issues, we introduce MIND, a multimodal framework that allows Large
Vision-Language Models (LVLMs) to infer purchase intentions from multimodal
product metadata and prioritize human-centric ones. Using Amazon Review data,
we apply MIND and create a multimodal intention knowledge base, which contains
1,264,441 million intentions derived from 126,142 co-buy shopping records
across 107,215 products. Extensive human evaluations demonstrate the high
plausibility and typicality of our obtained intentions and validate the
effectiveness of our distillation framework and filtering mechanism. Additional
experiments reveal that our obtained intentions significantly enhance large
language models in two intention comprehension tasks.
