Automated machine learning
Amazon Personalize takes care of machine learning for you. Once you have provided your data via Amazon S3 or via real-time integrations, Amazon Personalize can automatically load and inspect the data, enables you to select the right algorithms, train a model, provide accuracy metrics, and generate personalized recommendations. As your data set grows over time from new metadata and the consumption of real-time user event data, your models can be retrained to continuously provide relevant and personalized recommendations.
Make your recommendations relevant by responding to the changing intent of your users in real time.
Compute recommendations for very large numbers of users or items in one go, store them, and feed them to batch-oriented workflows such as email systems.
New user and new item recommendations
Effectively generate recommendations even for new users and find relevant new item recommendations for your users.
Improve relevance of recommendations by generating them within a context, for instance device type, time of day, and more.
Similar item recommendations
Improve the discoverability of your catalog by surfacing similar items to your users.
Easily integrate with your existing tools
Amazon Personalize can be easily integrated into websites, mobile apps, or content management and email marketing systems, via a simple inference API call. The service lets you generate user recommendations, similar item recommendations and personalized re-ranking of items. You simply call the Amazon Personalize APIs and the service will output item recommendations or a re-ranked item list in a JSON format, which you can use in your application.
GetRecommendations API - returns a list of relevant items given a userID. A representative usage example would be a content recommendation widget on landing page of a video streaming website that suggests a list of videos based on the user’s past watches. The API can also be used to return a list of similar itemIDs given an input itemID. A representative use case is to recommend similar movies when a user is on the detail page of a movie.
GetPersonalizedRanking - API re-ranks a list of itemIDs given a userID and a list of itemIDs to be re-ranked. The input list can be from any source, for example from an editorially curated list or from a list of itemIDs resulting from a search query. For example, an ecommerce retailer can use what they know about their customers’ previous behavior and past purchases to show the most relevant results, instead of showing the list of products that directly match the keyword.