"Vector search is foundational for building recommendation systems because it enables identifying similarities in user preferences and content attributes. By representing both users and items as vectors in a multi-dimensional space, vector search calculates their semantic proximity to suggest relevant recommendations. This approach ensures a more personalized user experience compared to traditional keyword matching.
For instance, in movie recommendation systems, user preferences, viewing history, and movie metadata are encoded into vectors. By finding the nearest neighbors to a user's vector, the system recommends movies with similar characteristics, genres, or themes. This semantic understanding allows for nuanced suggestions like recommending a documentary to someone who enjoyed science-related content.
Another example is in e-commerce, where vector search powers product recommendations. By comparing user browsing or purchase history with item vectors, the system identifies products that align with the user's interests. This capability extends to cross-selling strategies, such as suggesting accessories for a recently purchased device."