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The practice of using knowledge graphs and deep learning to develop a fast and scalable multimodal search engine for fashion E-Commerce.
With the recent advancements in deep-learning models, we wanted to introduce a new multimodal fashion search paradigm designed to help users interact with an online catalog using different modalities: speech, text, and images.
We traditionally build product knowledge graphs using schema.org to exploit metadata in modern SEO and digital marketing. By adding semantic markup we help search engines understand the products being sold and the audience that best fits the offering. Can we use this same approach to create a novel search experience for users landing on a fashion E-Commerce website? Is doing SEO on scale helpful to create a new search experience?
In this Masterclass, we will learn how to use product knowledge graphs to build a multimodal search engine that can more naturally help users find the product they want.
After this Masterclass participants will be more aware of structured linked data, neural search, and their impact in the E-Commerce domain. They will be equipped with concrete strategies and techniques to leverage existing data - within their organization - for improving their content discovery and search functionality while doing SEO.
We will also cover some essential elements of neural search using Jina AI and a combination of OpenAI’s CLIP (for image-to-text) and DistilBERT (for semantic text search).
Get hands-on experience using product knowledge graphs and neural search to develop a multimodal search system.
From product feeds to multimodal search:
Intermediate - Advanced
Google Colab and WordPress/WooCommerce will be used.
You need an access pass to attend this session: Diversity Access Pass or Full Access Pass apply