Aimed at companies that want to offer highly personalized product recommendations at scale, Sherpa.ai Custom Content Recommendation allows you to build ad hoc product and service recommendations and parameterize your business rules.
Thanks to Sherpa.ai’s general purpose technology, you can build recommendations within your catalog of products and services, such as:
- Any type of Retail Products
The Sherpa.ai Custom Content Recommender API uses AI models to make recommendations from the data you provide.
There are three basic types of entities that make up the recommender system:
- Users: The subjects that receive the recommendations. For instance, the readers of a newspaper or the customers of a shop are good candidates to be considered users in the API. Users can have attributes that characterize them, and those attributes can be used to filter users and seek similarities among them.
- Items: The objects that are part of a product or service catalog. The articles in a newspaper or the clothes sold by a shop are item-type entities. Items can have attributes that characterize them, and those attributes can be used to filter items and seek similarities among them. Items are organized in tables.
- Interactions: The different types of relationships that can be established between users and items. For example, reading a news article or buying a piece of clothing are interactions. Interactions have a weight associated, which is dependent upon the relevance you give them.
From the graph theory point of view, users and items would be the nodes and the interactions would be the type of edges that join those nodes. Once those entities are defined, you can establish the actual edges of the graph, which are the key objects for building recommendations. A user (e.g., a customer) can interact with an item (e.g., a shirt) in a given manner (e.g., buy, rate, etc.) at a given time. This user-item interaction can also have a value associated with it (e.g., the customer can buy the shirt or just rate it 3 out of 5).
The basic workflow to create recommendations using catalog content follows these steps:
The tutorials provide step-by-step examples of how to proceed and begin working with the API.
The goal of the Sherpa.ai Custom Content API is to provide a recommender system that adapts to any kind of product or service catalog. In order to cover the wide variety of data types, the API currently offers the following recommender engines: