Next Best Action
Maximize brand engagement and loyalty by proactively recommending actions tailored to your individual users’ needs in real-time. Next-Best-Action (aws-next-best-action) recipe generates recommendations for actions that your users are most likely to take based on their past behavior. Use Next-Best-Action recipe to recommend high-value actions such as enrolling in loyalty programs, signing up for a newsletter, exploring a new category, downloading an app, etc. Learn more.
Amazon Personalize offers intelligent user segmentation so you can run more effective prospecting campaigns through your marketing channels. With our two simple 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 while "aws-item-attribute" identifies users based on the attributes they care about, such as genre or price point. Intelligent user segmentation can drive higher engagement with marketing campaigns, increase retention through targeted messaging, and improve the return on investment for your marketing spend. Learn more.
Domain optimized recommenders
Recommendations tailored specifically for common use cases in industries such as Retail and Media & Entertainment make it faster and easier to deliver high-performing, personalized user experiences. You can choose from use cases like “Frequently Bought Together,” “Trending Now,” “Because You Watched X,” “Top Picks for You,” and more. You can map your data to a recommender that applies to your business need while Amazon Personalize chooses the optimal settings for your use case and automates the work of creating and maintaining personalized recommendations, accelerating time to market. Learn more.
The User-Personalization (aws-user-personalization) recipe is optimized for all personalized recommendation scenarios. It predicts the items that a user will interact with based on interactions, items, and user datasets. When recommending items, it uses automatic item exploration to improve discovery and engagement.
Similar item recommendations
Improve the discoverability of your catalog by surfacing similar items to your users. The Similar-Items (aws-similar-items) generates recommendations for items that are similar to an item you specify. Use Similar-Items to help customers discover new items in your catalog based on their previous behavior and item metadata. Recommending similar items can increase user engagement, click-through rate, and conversion rate for your application.
Personalized ranking is a list of recommended items that are re-ranked for a specific user. This is useful if you have a collection of ordered items, such as search results, promotions, or curated lists, and you want to provide personalized re-ranking for each of your users. Amazon Personalize enables you to highlight anything that could be relevant to your users while achieving your business priorities and ensuring the best customer experience.
New item recommendations
One of the most challenging problems in building relevant recommendations is offering the right recommendations when new items are added to your catalog. Amazon Personalize allows you to generate quality recommendations for new products and fresh content by creating the appropriate balance between recommendations for new and existing items in your catalog. Learn more.
The Trending-Now recommender helps customers recommend items gaining popularity at the fastest pace among their users. Customers can also define the frequency at which it identifies trending items, with options for refreshing recommendations every 30 mins, 1 hour, 3 hours or 1 day, based on the most recent interactions data from users.
Personalize your search results with Amazon Personalize and OpenSearch integration. Search is crucial in engaging users as it brings high-intent traffic from individuals seeking specific products. Improve the relevance of your search results by incorporating the unique interests and needs of each user through ML-based intelligence from Amazon Personalize. Using the Amazon Personalize Search Ranking plugin within OpenSearch v2.9 and above, you can boost relevant items in a specific user's search results based on their interests, context, and past interactions in real-time. You can also control the level of personalization for each search query to ensure maximum flexibility and control over your search experience. Personalizing your search results can increase user engagement, click-through rate, and conversion rate for your application.
Business rules and filters
Apply business rules to deliver the optimal customer experience. Amazon Personalize enables you to automatically augment recommendations. For example, filter out recently purchased items, highlight premium content if a user is in a particular subscription tier, or ensure 20% for a carousel is filled with trending sports articles. Dynamic filters allow you to modify filter rules on the fly without having to create separate permutations. Learn more.
Promote specific items or content based on rules that align with your business goals. With this feature, you can control the percentage of promoted content within your recommendations to further customize each user’s experience. Amazon Personalize automatically finds the most relevant items or content to be promoted for each user within the business rule provided and distributes it within the user's recommendations. Learn more.
Unstructured text support
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. Learn more.
Business metric optimization
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 time spent on a platform, user engagement, profit margin, revenue, or any numerical metric you define as important to your business. Learn more.
Real-time or batch recommendations
Amazon Personalize provides flexibility to use real-time or batch data based on what is most appropriate for your use case. For example, real-time data may be more appropriate for product or content recommendations on a website or app. Make your recommendations relevant by responding to the changing intent of your users in real-time. Batch data may be more appropriate for large notification campaigns. For instance, you can 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. Amazon Personalize now supports incremental bulk data imports; a new option for updating your data and improving the quality of your recommendations. You can easily append new records to the existing data in your datasets. Learn more.
Providing relevant recommendations requires you to consider the context in which they are bring viewed. With contextual recommendations, you can deliver a more personalized experience for customers and improve the relevance of recommendations by generating them within a context, for instance device type, time of day, and more. Learn more.
Measure recommendation impact
Amazon Personalize enables customers to automatically understand the impact of Personalize on their business objectives such as cart-adds, page views, and clicks. Customers can measure the business outcome of any Personalize recommendation by calculating the impact of any event sent to the system. When a user completes an action (i.e. event), that data is sent to Personalize and the total impact is calculated. Learn more.
Generative AI capabilities
Amazon Personalize Content Generator uses generative AI to make recommendations more compelling with text generated by foundation models. It improves personalization by accompanying each recommendation with a tailored snippet that describes the thematic similarity between recommended items. Incorporate it in website carousels and email campaigns to replace generic titles like “More like X”, fostering a deeper connection with your end users. Learn more.
Builders can use a custom chain on LangChain to seamlessly integrate Amazon Personalize with generative AI solutions. With pre-configured LangChain code, you can invoke Amazon Personalize, retrieve recommendations for a campaign or a recommender, and seamlessly feed it into your generative AI applications within the LangChain ecosystem. Explore a range of use cases including personalized marketing copy, recommending products or content in chatbots, or generating concise summaries for personalized content. Learn more.
Return metadata in inference response
Amazon Personalize improves your generative AI workflow by enabling return item metadata as part of the inference output. Select up to 10 fields, such as genre, rating, and product description, and use our LangChain integration capability to seamlessly feed these enriched recommendations into the foundation models. This richer context helps models generate highly personalized content to boost your engagement with end users. Learn more.
Refer to the developer guide for instructions on using Amazon Personalize.
Instantly get access to the AWS Free Tier.
Get started building with Amazon Personalize in the AWS Management Console.