Two-Tower Recommender System for E-Commerce

The Two-Tower Recommender System is a machine learning/neural network-based recommendation engine designed to provide personalized product recommendations. It uses two separate neural network architectures: the Query Tower processes user input queries (e.g., search text or descriptions) to generate embeddings, while the Product Tower processes product data (e.g., descriptions, features) to create product embeddings. The system calculates the similarity between the query and product embeddings to recommend the most relevant products. It leverages techniques such as text embedding, neural networks, and similarity metrics like cosine similarity to match user queries with products in the catalog.

The Two-Tower Recommender System is a machine learning/neural network-based recommendation engine designed to provide personalized product recommendations. It uses two separate neural network architectures: the Query Tower processes user input queries (e.g., search text or descriptions) to generate embeddings, while the Product Tower processes product data (e.g., descriptions, features) to create product embeddings. The system calculates the similarity between the query and product embeddings to recommend the most relevant products. It leverages techniques such as text embedding, neural networks, and similarity metrics like cosine similarity to match user queries with products in the catalog.