vectorembeddings

Vector embeddings are continuous vector representations of discrete items, such as words, sentences, documents, images, or users. They map items into a high-dimensional space where semantic or functional similarity is reflected by proximity in the vector space. The goal is to capture structure that is useful for machine learning models, enabling efficient similarity queries, clustering, and downstream prediction tasks.

Common types include static word embeddings, such as Word2Vec and GloVe, which assign a single vector per word. Contextual embeddings, produced by models like BERT and GPT, generate different vectors for a word depending on its surrounding text. Sentence and document embeddings are used for capturing broader meaning, with approaches like Sentence-BERT or Doc2Vec. Image embeddings arise from convolutional networks such as ResNet. Multimodal embeddings integrate signals from multiple modalities, aligning text and image representations in a shared space.

Embeddings are typically learned through self-supervised or unsupervised objectives, including predicting neighboring tokens, reconstructing inputs, or contrasting positive and negative samples. Fine-tuning on downstream tasks is common to tailor the representations to a specific domain or application. Evaluation often combines intrinsic methods, such as word similarity or analogy tests, with extrinsic assessments on classification, retrieval, or clustering tasks. Distances like cosine similarity or Euclidean distance quantify relationships, sometimes with normalization to improve comparability.

Applications span search and information retrieval, recommendations, semantic similarity, paraphrase detection, clustering, and transfer learning for various predictive models. Limitations include biases inherited from training data, interpretability challenges, domain shift, computational cost, and privacy concerns. Related topics include embedding methods, representation learning, dimensionality reduction, and similarity metrics.