Multimodal Search for E-commerce: Image + Text + Attributes | ML Knowledge Hub

Dashboards track metrics like zero-result rates, click-through rates, conversion rates, and latency to monitor performance.

The product catalog is vectorized by normalizing attributes; this allows for efficient similarity comparisons between products based on their textual descriptions and visual features.

Building a multimodal retrieval system involves combining initial retrieval with a basic reranking step to improve the quality of results.

Behavior-based re-rankers utilize user interaction data (clicks, purchases) to refine search results. Deduplication removes redundant products from the ranked list, while explainability provides users with insights into why a particular product was recommended.