Semantic Ad Matching via Siamese Neural Networks

The patent replaces conventional keyword matching mechanisms with a Siamese neural network-based semantic matching system. The system uses deep learning models to extract semantic vector representations from product titles and descriptions, then calculates cosine similarity to determine advertisement relevance. This substitution of mechanical keyword matching with intelligent semantic analysis significantly improves advertisement relevance while accepting increased system complexity.

The patent implements pre-computation of semantic vector representations for all products in the catalog. During the training phase, the Siamese neural network processes product titles and descriptions to generate and store semantic vectors in advance. When a user views a product, the system only needs to calculate cosine similarity between the user's current product vector and pre-computed advertisement vectors, significantly reducing real-time processing time while maintaining high advertisement relevance.

The patent merges multiple data sources including product titles, product descriptions, and metadata into a unified semantic representation. The Siamese neural network processes these combined text inputs to generate comprehensive semantic vectors that capture the full meaning and context of products. This integration of multiple data elements significantly improves matching accuracy by considering both explicit keywords and implicit semantic relationships, while the unified processing approach manages the complexity through a single model architecture.