Use case optimized recommenders for retail and media and entertainment

Introducing new recommenders that make it faster and easier to deliver high-performing personalized user experiences. You can choose from use cases like “Frequently Bought Together,” “Because You Watched X,” “Top Picks for You,” and more. Map your data to a recommender; Amazon Personalize chooses the optimal settings for your use case and automates the work of creating and maintaining personalized recommendations. For sample cost calculations, see Amazon Personalize pricing.

User segmentation

Amazon Personalize now offers intelligent user segmentation so you can run more effective prospecting campaigns through your marketing channels. With our two new recipes, you can automatically segment your users based on their interest in different product categories, brands, and more. aws-item-affinity identifies users based on their interest in individual items, such as movies, songs, or products. aws-item-attribute identifies users based on the attributes they care about, such as genre or price point. This drives higher engagement with marketing campaigns, increases retention through targeted messaging, and improves the return on investment for your marketing spend. For sample cost calculations, see Amazon Personalize pricing

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, let you to select the right algorithms, train a model, provide accurate 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. Learn more.

Real-time recommendations

Make your recommendations relevant by responding to the changing intent of your users in real time. Learn more.

Batch recommendations

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. Learn more.

New user and new item recommendations

Effectively generate recommendations even for new users and find relevant new item recommendations for your users.

Contextual recommendations

Improve relevance of recommendations by generating them within a context, for instance device type, time of day, and more. Learn more.

Similar item recommendations

Improve the discoverability of your catalog by surfacing similar items to your users.

Unlock information in unstructured text

Unlock the information trapped in product descriptions, reviews, movie synopses, or other unstructured text to generate highly relevant recommendations for users. Provide unstructured text as part of your catalog, and Amazon Personalize automatically extracts key information to use when generating recommendations. Supported languages include Chinese (Simplified and Traditional), English, French, German, Japanese, Portuguese, and Spanish.

Prioritizing your business goals and what is relevant for your users

Consider what’s relevant to your users and what is important for your business when generating recommendations. You can define an objective, in addition to relevance, to influence recommendations. This can be used to maximize for streaming minutes, increase revenue lift, or any metric you define as important to your business.

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 the 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, such as 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.

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Refer to the developer guide for instructions on using Amazon Personalize.

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