Scann

Generate vector embeddings for all content items.

Index the embeddings using Scann.

When a user interacts with an item, search the Scann index for similar items.

Recommend the top N similar items to the user.

Extract feature vectors from all images in the database using a pre-trained CNN.

Index the feature vectors using Scann.

Extract the feature vector from the query image.

Search the Scann index for similar images based on the query vector.

Return the top N similar images.

Generate vector embeddings for each item in the dataset.

Index the embeddings using Scann.

For each item, search the Scann index for its nearest neighbors.

Flag items that are very close neighbors as potential duplicates.

Generate vector embeddings for each document.

Index the embeddings using Scann.

Use Scann to perform k-means clustering on the document embeddings.

Assign each document to the cluster with the nearest centroid.