Embeddings

Embeddings transform complex data (e.g., words, images, sounds) into a simpler numerical form understandable by computers. They serve as a bridge, translating human language and visual information into numbers.

Why Use Embeddings?

• Similarity: Words or images with similar meanings or appearances have vectors that are close together. This aids in tasks like finding synonyms or similar images. Therefore, vector embeddings for ‘dog’ and ‘cat’ will be close, while the embedding for a ‘sparrow' will be much farther away.

• Context: Embeddings capture the context of words or images. For instance, the word "play" will have different embeddings in the context of chess, a stereo system, or a theatre.

Use Cases

Thus, embeddings have useful applications for businesses such as:

1. When a customer views a dress on an online fashion retailer's site, the "You may also like" section could suggest complementary shoes, bags, and accessories.

2. A video/music streaming platform provides personalized content recommendations for users based on their viewing/listening history, ratings, and interactions with the platform.

3. An ecommerce platform can analyze sudden drops in purchase frequency or negative interactions with customer support to identify potential customer churn, allowing for timely remedial actions.