How To Build a Real-Time Product Recommendation System Using Redis and DocArray

This tutorial helps build a real-time product recommendation system using content-based filtering and vector similarity search, leveraging tools like Redis and DocArray. Recommendation systems are crucial for e-commerce sites to generate good conversions and maintain customer loyalty. The system uses CLIP-as-service to encode visual data and exploits all modalities of the data by modeling user and items as feature vectors. Vector similarity is computed in real-time using efficient techniques such as Hierarchical Navigable Small World (HNSW), implemented in vector databases like Redis. DocArray serves as a universal vector database client with support for multimodal data, making it easy to build a recommendation system in just a few lines of code. The procedure involves provisioning a Redis instance, installing necessary tools, and assembling the tools for the application. The tutorial demonstrates how to create a weighted average of the embeddings of recently-viewed items to recommend products based on user filters and view history, taking into account the importance of recent items.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.