Applications of Vector Databases

Applications of vector databases increase as time passes. Let's discuss those applications identified during research, where developers can take advantage of their unique value.

Recommender systems

Have you ever spent a lot of time scrolling through Netflix, trying to decide which movie to watch? Or have you felt confused while searching for a product online? Social media offers a lot of content these days. which can be helpful, but can also be confusing.

Recommendation systems work to sort out these problems. They intelligently learn from what we've watched and the types of products we've purchased. They gather this information and provide recommendations based on our preferences. Interestingly, smartphones can sometimes recommend items based on our conversations and places we visit. It may sound unreal, but it happens.

Vector databases power recommendation systems by efficiently finding items similar to a user's past preferences or current search. This personalizes the user experience on e-commerce platforms, streaming services, and more. For example, a movie vector might consider genre, actors, director, and user reviews. A product vector in an e-commerce store might include details like brand, category, color, and previous purchase history. These vectors are stored efficiently in the vector database.

How vector databases work in recommender systems

Let's see how a vector database works in a recommender system:

• Vector databases store item and user data as high-dimensional vectors, encapsulating their key features. The core strength lies in the ability to rapidly compare these vectors.

• The recommendation system builds a user vector based on their past interactions.

• The system uses the vector database's search capabilities to find items with vectors closest to the user's vector.

• This closeness in vector space signifies a high degree of similarity, allowing the system to recommend these relevant items to the user.