The answer
Yes. A multimodal model can include different input encoders and train their outputs to interact or share a compatible space, depending on its objective.
Yes. A multimodal model can include different input encoders and train their outputs to interact or share a compatible space, depending on its objective.
No. They can also use images, structured attributes, user interactions, or learned item identifiers. The appropriate signals depend on the recommendation or retrieval task.
Exact constraints are better handled with structured data and explicit comparisons. An embedding may retrieve a related product while failing to preserve a crucial numerical difference.
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