Recommendation Engines and Visual Search

On some of the largest commercial platforms, recommendations account for as much as 30% of revenue, which can translate into billions of dollars in sales. That’s why retailers are using recommender systems to drive every action shoppers take, from visiting a web page to using social media for shopping. They also improve conversion by offering up relevant consumer products from the exponential number of available options. 

NVIDIA Merlin, an end-to-end recommender-on-GPU framework, provides fast feature engineering and high training throughput to enable fast experimentation and production retraining of DL recommender models. Merlin also enables low latency, high-throughput, production inference.

Retailers are leveraging the next generation of computer vision for sophisticated image attribute recognition to automatically generate comprehensive meta-tagging and cataloging. Access to comprehensive information about products and services helps identify images, resulting in a successful personalized recommendation system. 

Since fashion changes quickly, NVIDIA partner Omnious offers an AI-tagging API that helps B2B customers stay ahead of the fashion curve. Ominous Tagger, the automated tagging solution with over 95% accuracy, is 100X faster than manual tagging and increases search efficiency by 4X. Omnious also offers a trend report that analyzes social media fashion influencer images.