Multi-modal clothing recommendation model based on large model and VAE enhancement

This contrasts with traditional models that process text in a single direction, and it has been widely demonstrated that BERT effectively captures contextual and semantic relationships in text, thereby providing a more comprehensive understanding of context. The embedding components of BERT include word embeddings, segment embeddings, and position embeddings. In essence, word embeddings map each word individually into a vector within a high-dimensional space. The segment embeddings allow BERT to differentiate and process single texts or pairs of texts, thereby enabling the understanding of semantic information at the sentence level. The position embeddings provide sequential information to the structure, allowing the model to mark the position of words in a sentence, which aids in further processing at a higher level. Finally, the CLS token at the beginning of the input sequence represents the final hidden state in the embedding vector, which is commonly used as the representation of the entire input sequence.