value insights

Powering Personalized Shopping in 2024: A Deep Dive into Google’s Shopping Graph

Ever wondered how Google manages to present you with such a vast and relevant selection of products when you search for something online? The secret lies in a powerful tool called the Shopping Graph.

Imagine a giant digital knowledge base, not unlike Google’s Knowledge Graph for entities and places, but specifically designed for products. This intricate network connects information about billions (over 45 billion to be exact!) of products from all corners of the web. It’s constantly buzzing with activity, thanks to Google’s AI and real-time updates. Here’s how it works under the hood:

  • Data Ingestion Pipeline: The Shopping Graph is a complex system that constantly ingests data from a variety of sources. This includes structured data feeds from online stores (think product specifications in standardized formats), unstructured data like product descriptions and reviews scraped from websites, and even social media mentions to capture current trends and user sentiment.

  • Entity Recognition and Normalization: Not all product information is created equal. The Shopping Graph employs advanced techniques like Named Entity Recognition (NER) to identify key product attributes within this diverse data stream. Imagine a product description mentioning a “15.6-inch Full HD display.” NER would recognize “15.6” and “Full HD” as relevant attributes and categorize them accordingly. Normalization then ensures consistency – for example, converting various ways of expressing screen size (e.g., “15.6in” or “15.6 inch”) into a standard format for easy comparison.

  • Knowledge Graph Integration: The Shopping Graph leverages the power of Google’s existing Knowledge Graph. This allows it to connect products to broader concepts and categories. For instance, if a product listing mentions “hiking boots,” the Shopping Graph can link it to the “hiking gear” category within the Knowledge Graph, potentially suggesting complementary items like backpacks or tents.

  • Machine Learning and Ranking: At the heart of the Shopping Graph lies a sophisticated machine learning model. This model analyzes vast amounts of data, including user search queries, browsing behavior, product attributes, and historical sales information. Using complex algorithms, it ranks products based on their relevance to a specific search query, user preferences, and overall popularity. This ensures that users see the most relevant options at the top of their search results.

Here’s a real-world example, with some figures to illustrate the scale:

  • Let’s say you’re searching for a “gaming laptop with an RTX 3070 graphics card.” The Shopping Graph can process your query in milliseconds, leveraging its massive dataset of potentially millions of laptop listings.
  • Entity Recognition: It identifies “gaming laptop” and “RTX 3070” as key attributes.
  • Normalization: It ensures all listings with compatible graphics cards are included, regardless of how the manufacturer phrased it (e.g., “RTX 3070” or “NVIDIA GeForce RTX 3070”).
  • Ranking: The machine learning model then takes over. It considers factors like user reviews for gaming performance, brand reputation, and historical sales data for RTX 3070 laptops. Based on this analysis, it ranks the most relevant laptops at the top of your search results, potentially highlighting models with high user ratings for gaming or those with the best current price based on real-time updates from retailers.

By creating a constantly updated and interconnected database of products, the Shopping Graph is revolutionizing the way we shop online. It empowers users with more relevant choices, saves them valuable time, and paves the way for a more personalized and enjoyable shopping experience.

Google testing new tools for a more personalized shopping experience

Google is piloting new product discovery elements designed to give users a more personalized shopping experience, including:

  • Style recommendations.
  • Brand preferences.
  • Generative AI for product search.
  • Virtual try-on technology.

Why . These features offer businesses new opportunities to showcase their products and engage with high-value consumers who are likely to be more inclined to make purchases, thanks to a personalized shopping experience tailored to their specific preferences.

Style recommendations. Google is testing Style Recommendations, now accessible to signed-in U.S. shoppers via mobile browsers and the Google app, aiming to deliver more personalized results. For instance, when users search for specific apparel like shoes, they’ll come across the Style Recommendations section. Here, they can offer ratings with thumbs up or thumbs down, or simply swipe right or left to immediately access personalized results.

How it works. If shoppers aren’t satisfied with their personalized results or would prefer to continue browsing, Google offers the option to rate more items and immediately view another set of results. Additionally, the search engine will remember preferences for future searches. For example, when searching for men’s polo shirts again, you’ll receive personalized style recommendations based on your past preferences and interactions with products.

Managing style recommendations. If you’ve rated items incorrectly or would prefer to not see personalized shopping results, you can manage your preferences. To do this, just tap the three dots next to the “Get style recommendations” section and explore personalization options in the “About this result” panel.

Brand preferences. U.S. shoppers searching for apparel, shoes, or accessories on mobile browsers, desktop, or the Google app can now personalize their shopping experience by selecting preferred brands. Once chosen, options from these brands will appear instantly. To manage preferences, tap the three dots next to the “Popular from your favorite brands” section, and access personalization options in the “About this result” panel.


Image generation. Google is piloting an AI image generation tool for shopping, which is now available to all U.S. users who have opted into Search Generative Experience (SGE) within Search Labs. If you’re searching for a specific item, like a “colorful quilted spring jacket,” simply tap “Generate images” after your search to see photorealistic options matching your preferences. Once you find one you like, click on it to explore shoppable options conveniently.

Virtual try on. Additionally, Google is testing a virtual try-on tool in the U.S. on desktop, mobile and the Google app. When you see the “try-on” icon in shopping results, simply click on it, and you can see what the product looks like on a diverse set of real models ranging in size from XXS-4XL.


What Google is saying. Sean Scott, Google’s VP/GM Consumer Shopping, said:

  • “People shop on Google more than a billion times a day. And thanks to the Shopping Graph, they see billions of products in their results that are constantly being refreshed. In fact, every hour, more than 2 billion listings are updated with the latest information, including pricing, in-stock availability and shipping details.”
  • “So whether you’re looking for a specialty notepad from Japan, a monogrammed handbag from Paris or just a hammer from your local hardware store, the Shopping Graph can help you find it in just a few clicks. With that many options, we’re focused on making it easy to find exactly what you like.”
  • “No two shoppers are alike, which is why we’re designing the shopping experience on Google so it’s tailored to you.”

Exploring Potential Uses of the Shopping Graph

The Google Shopping Graph is a powerful tool that’s already transforming the way we shop online. But its potential extends far beyond just personalized search results and real-time pricing. Here’s a glimpse into some exciting possibilities for the Shopping Graph’s future:

  • Predictive Shopping: Imagine your fridge automatically creating a shopping list based on expiring items and your past purchases. The Shopping Graph, integrated with smart home devices, could analyze your habits and suggest relevant products from nearby stores.
  • Visual Search Revolution: The Shopping Graph could become the backbone of a powerful visual search experience. Imagine snapping a picture of your friend’s stylish jacket and instantly finding similar options online.
  • Sustainable Shopping: The Shopping Graph could be harnessed to promote eco-friendly choices. By highlighting products made from recycled materials or with sustainable practices, it could empower users to make informed decisions.
  • Hyperlocal Shopping: The Shopping Graph’s real-time inventory data could be a boon for local businesses. Imagine searching for a specific book and seeing not just online retailers but also independent bookstores in your area that have it in stock.
  • Combating Counterfeits: The power of the Shopping Graph’s data analysis could be used to identify and flag counterfeit products, ensuring users have access to genuine goods.

These are just a few ideas – the possibilities are truly endless. As the Shopping Graph continues to evolve and integrate with other AI advancements, we can expect even more innovative ways to shop, discover products, and make informed purchasing decisions. The future of shopping is personalized, convenient, and driven by intelligent data – and the Google Shopping Graph is at the forefront of this exciting revolution.

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