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Exploring Personalized Shopping Experiences with Qdrant’s Discovery API
Improve personalized product discovery with Qdrant’s Discovery API on a Streamlit web app
Introduction
In the rapidly growing e-commerce landscape, delivering personalized shopping experiences has become one of the key differentiators for success. In this article, we dive deep into the functionality of Qdrant’s new Discovery API, mainly focusing on context search, to unlock tailored product recommendations. To demonstrate this new approach, I’ll walk you through an implementation of a Streamlit App that I have developed, seamlessly integrating with Qdrant’s vector database.
Trust me, it’s fun! ✌️
Discovery API
Qdrant’s Discovery API introduces the concept of “context,” which is a powerful tool for splitting the vector space efficiently, where the context comprises positive-negative pairs, effectively dividing the space into sub-zones based on given user preferences. The search mechanism in this context gives priority to points that actually belong to the positive zones while avoiding the negative ones.
There are mainly 2 types of search:
1. Discovery Search: Utilizes target and context pairs to find points that are closest to the target but…
