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, Recommendations AI builds a recommendation model specific to your company. You can then request recommendations for other catalog products to display to your users.
In order to build recommendation machine learning models, Recommendations AI 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.
The Recommendations AI API
The Recommendations AI API provides capabilities for two tasks:
Data Ingestion: You can upload and manage product catalog information and user event logs for your websites. Recommendations AI uses this information to train and update recommendation models.
Prediction: You can request recommendations based on your product catalog and user event logs.
For more information about the process of implementing Recommendations AI for your website, see Implementing Recommendations AI.