This is the unified documentation for Retail API. This includes Recommendations AI, Retail Search, and the unified Retail console (which is applicable to both Recommendations AI and Retail Search users). To use the new console or Retail Search while they are in the restricted GA phase, submit a form here to contact Cloud sales. If you are using the v1beta version of Recommendations AI, migrate to the GA version: Migrating to the Retail API from beta.

To see documentation for only Recommendations AI and the Recommendations AI-only console, go to the How-to guides for Recommendations AI and the API reference documentation for Recommendations AI.

Features and capabilities of the Retail API

Recommendations AI and Retail Search use the Retail API. You can use the Retail API to upload and manage product catalog information and user event logs for your websites. You can get and customize results based on this information, and the Retail API continues to use this data to train and update models that improve your predictions and search results.

For more information about the process of implementing Retail for your website, see Implementing Retail.

Recommendations AI

Recommendations AI enables you to build high quality personalized product recommendation systems without requiring a high level of expertise in machine learning, systems design, or operations. Leveraging your website's catalog products and user behavior, the Retail API builds a recommendation model specific to your company. You can then request recommendations for other catalog products to display to your users.

The Retail API uses user events and your product catalog to train recommendation machine learning models, which provide recommendations based on this data.

Recommendations AI capabilities include:

  • Custom models. Each model is trained specifically for your data, based on sequence-based machine learning models using transformers.

  • Personalized results. Leverage personalization algorithms without any machine learning expertise. Recommendations are based on user behavior and activities like views, clicks, and in-store purchases as well as online activity, so that every prediction result is personalized.

  • Real-time predictions. Each recommendation served considers previous user activity like click, view, and purchase events, so recommendations are in real time.

  • Automatic model training and tuning. Daily model retraining ensures all the models can accurately capture user behavior every day.

  • Optimization objectives. Goals like conversion rate, click-through rate, and revenue optimization help you precisely optimize for your business goal.

  • Omnichannel recommendations. With the API model, go beyond website recommendations to personalize your entire shopper journey to recommendations on mobile apps, personalized email recommendations, store kiosks, or call center applications.

Retail Search enables you to provide high quality product results that are customizable for your business needs. Leverage Google's query and contextual understanding to improve product discovery across your website and mobile applications.

Retail Search capabilities include:

  • Product hierarchies: You can include collections and variants in your searchable product catalog.

  • Query expansion: Increase the relevant results returned for query terms that would normally produce fewer results, such as queries that use very specific keywords.

  • Relevance thresholding: Adjust how Retail balances returning precision (the relevance of the search results returned) and recall (returning more results for that query).

  • Pagination: Control pagination of your search results to decrease lookup time and response size.

  • Filtering: Use expression syntax to provide filtering that refines your site's search results.

  • Ordering: Set the order of search results by multiple fields in order of priority.

  • Faceting: Generate faceting to provide more relevant options to your users based on attributes you provide. Buckets need to be provided for numerical attributes in the search request to return them in the search response.

  • Dynamic faceting: Automatically generate facet keys based on search queries and automatically combine (and rerank) with facet keys provided in the search request. This feature is currently based on an allowlist. Contact Retail Search support for help enabling this feature.

  • Boosting and burying: Control search result ranking by prioritizing or deprioritizing some types of results.

  • Browsing: Get search results based on categories provided in the search request. Note that the query field is empty in this mode. This can be combined with filtering, ordering, faceting, dynamic faceting, boosting, and burying. This feature is currently based on an allowlist. Contact Retail Search support for help enabling this feature.

Using the Retail API

In order to build machine learning models for recommendations or search, Retail needs two sets of information:

  • Product catalog: Information of the products sold to customers. This includes the product title, description, in-stock availability, pricing, and so on.

  • User events: End user behavior on your website. This includes users searching for, viewing, or purchasing a specific item, your website showing users a list of products, and so on.

With many integration options, you can ingest your data using tools you might already use, such as BigQuery, Cloud Storage, Merchant Center, Tag Manager, and Google Analytics.