Drive hyper-personalized customer experiences with Amazon Personalize and generative AI

Today, we are excited to announce three launches that will help you enhance personalized customer experiences using Amazon Personalize and generative AI. Whether you’re looking for a managed solution or build your own, you can use these new capabilities to power your journey.

Amazon Personalize is a fully managed machine learning (ML) service that makes it easy for developers to deliver personalized experiences to their users. It enables you to improve customer engagement by powering personalized product and content recommendations in websites, applications, and targeted marketing campaigns, with no ML expertise required. Using recipes (algorithms prepared for specific uses cases) provided by Amazon Personalize, you can offer diverse personalization experiences like “recommend for you”, “frequently bought together”, guidance on next best actions, and targeted marketing campaigns with user segmentation.

Generative AI is quickly transforming how enterprises do business. Gartner predicts that “by 2026, more than 80% of enterprises will have used generative AI APIs or models, or deployed generative AI-enabled applications in production environments, up from less than 5% in 2023.” While generative AI can quickly create content, it alone is not enough to provide higher degree of personalization to adapt to the ever-changing and nuanced preferences of individual users. Many companies are actively seeking solutions to enhance user experience using Amazon Personalize and generative AI.

FOX Corporation (FOX) produces and distributes news, sports, and entertainment content.

“We are integrating generative AI with Amazon Personalize in order to deliver hyper-personalized experiences to our users. Amazon Personalize has helped us achieve high levels of automation in content customization. For instance, FOX Sports experienced a 400% increase in viewership content starts post-event when applied. Now, we are augmenting generative AI with Amazon Bedrock to our pipeline in order to help our content editors generate themed collections. We look forward to exploring features such as Amazon Personalize Content Generator and Personalize on LangChain to further personalize those collections for our users.”

– Daryl Bowden, Executive Vice President of Technology Platforms.

Announcing Amazon Personalize Content Generator to make recommendations more compelling

Amazon Personalize has launched Content Generator, a new generative AI-powered capability that helps companies make recommendations more compelling by identifying thematic connections between the recommended items. This capability can elevate the recommendation experience beyond standard phrases like “People who bought this also bought…” to more engaging taglines such as “Rise and Shine” for a breakfast food collection, enticing users to click and purchase.

To explore the impact of Amazon Personalize Content Generator in detail, let’s look at two examples.

Use case 1: Carousel titles for movie collections

A micro-genre is a specialized subcategory within a broader genre of film, music, or other forms of media. Streaming platforms use micro-genres to enhance user experience by allowing viewers or listeners to discover content that aligns with their specific tastes and interests. By recommending media content with micro-genres, streaming platforms cater to diverse preferences, ultimately increasing user engagement and satisfaction.

Now you can use Amazon Personalize Content Generator to create carousel titles for micro-genre collections. First, import your datasets of users’ interactions and items into Amazon Personalize for training. You upload a list of itemId values as your seed items. Next, create a batch inference job selecting Themed recommendations with Content Generator on the Amazon Personalize console or setting batch-inference-job-mode to THEME_GENERATION in the API configuration.

As the batch inference output, you will get a set of similar items and a theme for each seed item. We also provide items-theme relevance scores that you can use to set a threshold to show only items that are strongly related to the theme. The following screenshot shows an example of the output:

{"input":{"itemId":"40"},"output":{
"recommendedItems":["36","50","44","22","21","29","3","1","2","39"],
"theme":"Movies with a strong female lead",
"itemsThemeRelevanceScores":[0.19994527,0.183059963,0.17478035,0.1618133,0.1574806,0.15468733,0.1499242,0.14353688,0.13531424,0.10291852]}}

{"input":{"itemId":"43"},"output":{
"recommendedItems":["50","21","36","3","17","2","39","1","10","5"],
"theme":"Romantic movies for a cozy night in",
"itemsThemeRelevanceScores":[0.184988,0.1795761,0.11143453,0.0989443,0.08258403,0.07952615,0.07115086,0.0621634,-0.138913,-0.188913]}}
...

Subsequently you can replace the generic phrase “More like X” with the output theme from Amazon Personalize Content Generator to make the recommendations more compelling.

Use case 2: Subject lines for marketing emails

Email marketing, although cost-effective, often struggles with low open rates and high unsubscribe rates. The decision to open an email critically depends on how attractive the subject line is, because it’s the first thing recipients see along with the sender’s name. However, scripting appealing subject lines can often be tedious and time-consuming.

Now with Amazon Personalize Content Generator, you can create compelling subject lines or headlines in the email body more efficiently, further personalizing your email campaigns. You follow the same process of data ingestion, training, and creating a batch inference job as in the previous use case. The following is an example of a marketing email that incorporates output from Amazon Personalize using Content Generator, including a set of recommended items and a generated subject line:

These examples showcase how Amazon Personalize Content Generator can assist you in creating a more engaging browsing experience or a more effective marketing campaign. For more detailed instructions, refer to Themed batch recommendations.

Announcing LangChain integration to seamlessly integrate Amazon Personalize with the LangChain framework

LangChain is a powerful open-source framework that allows for integration with large language models (LLMs). LLMs are typically versatile but may struggle with domain-specific tasks where deeper context and nuanced responses are needed. LangChain empowers developers in such scenarios to build modules (agents/chains) for their specific generative AI tasks. They can also introduce context and memory into LLMs by connecting and chaining LLM prompts to solve for varying use cases.

We are excited to launch LangChain integration. With this new capability, builders can use the Amazon Personalize custom chain on LangChain to seamlessly integrate Amazon Personalize with generative AI solutions. Adding a personalized touch to generative AI solutions helps you create more tailored and relevant interactions with end-users. The following code snippet demonstrates how you can invoke Amazon Personalize, retrieve recommendations for a campaign or recommender, and seamlessly feed it into your generative AI applications within the LangChain ecosystem. You can also use this for sequential chains. Refer to GitHub for installation instructions of the repo.

from aws_langchain import AmazonPersonalize
from aws_langchain import AmazonPersonalizeChain
from langchain.llms.bedrock import Bedrock

recommender_arn="<insert_arn>"
client=AmazonPersonalize(recommender_arn=recommender_arn, credentials_profile_name="default",region_name="us-west-2")

bedrock_llm = Bedrock(model_id="anthropic.claude-v2", region_name="us-west-2")

# Create personalize chain
chain = AmazonPersonalizeChain.from_llm( llm=bedrock_llm, client=client)
response = chain({'user_id': '1'})

You can use this capability to craft personalized marketing copies, generate concise summaries for recommended content, recommend products or content in chatbots, and build next-generation customer experiences with your creativity.

Amazon Personalize now enables you to return metadata in inference response to improve generative AI workflow

Amazon Personalize now improves your generative AI workflow by enabling return item metadata as part of the inference output. Getting recommendations along with metadata makes it more convenient to provide additional context to LLMs. This additional context, such as genre and product description, can help the models gain a deeper understanding of item attributes to generate more relevant content.

Amazon Personalize supports this capability for both custom recipes and domain optimized recommenders. When creating a campaign or a recommender, you can enable the option to return metadata with recommendation results, or adjust the setting by updating the campaign or recommender. You can select up to 10 metadata fields and 50 recommendation results to return metadata during an inference call, either through the Amazon Personalize API or the Amazon Personalize console.

The following is an example in the API:

## Create campaign with enabled metadata
example_name = 'metadata_response_enabled_campaign'
create_campaign_response = personalize.create_campaign(
    name = example_name,
    solutionVersionArn = example_solution_version_arn,
    minProvisionedTPS = 1,
    campaignConfig = {"enableMetadataWithRecommendations": True}
)

## GetRecommendations with metadata columns
metadataMap = {"ITEMS": ["genres", "num"]}
response = personalize_runtime.get_recommendations(campaignArn=example_campaign_arn,
     userId="0001", itemId="0002", metadataColumns=metadataMap, numResults=2)
     
## Example response with metadata
 itemList': 
 [
     {
      'itemId': '356',
      'metadata': {'genres': 'Comedy', 'num': '0.6103248'}
     },
     {
      'itemId': '260',
      'metadata': {'genres': 'Action|Adventure', 'num': '0.074548'}},
     }
 ]

Conclusion

At AWS, we are constantly innovating on behalf of our customers. By introducing these new launches powered by Amazon Personalize and Amazon Bedrock, we will enrich every aspect of the builder and user experience, elevating efficiency and end-user satisfaction. To learn more about the capabilities discussed in this post, check out Amazon Personalize features and the Amazon Personalize Developer Guide.

About the Authors

Jingwen Hu is a Senior Technical Product Manager working with AWS AI/ML on the Amazon Personalize team. In her spare time, she enjoys traveling and exploring local food.

Pranav Agarwal is a Senior Software Engineer with AWS AI/ML and works on architecting software systems and building AI-powered recommender systems at scale. Outside of work, he enjoys reading, running, and ice-skating.

Rishabh Agrawal is a Senior Software Engineer working on AI services at AWS. In his spare time, he enjoys hiking, traveling, and reading.

Ashish Lal is a Senior Product Marketing Manager who leads product marketing for AI services at AWS. He has 9 years of marketing experience and has led the product marketing effort for intelligent document processing. He got his master’s in Business Administration at the University of Washington.

Ketan Kulkarni is a Software Development Engineer with the Amazon Personalize team focused on building AI-powered recommender systems at scale. In his spare time, he enjoys reading and traveling.

Yifei Ma is a Senior Applied Scientist at AWS AI Labs working on recommender systems. His research interests lie in active learning, generative models, time-series analysis, and online decision making.

Anusha Lalitha is an Applied Scientist with AWS AI Labs supporting Amazon Personalize. Her research interests lie broadly in recommendation systems, stochastic bandits, and generative models.

Hao Ding is a Senior Applied Scientist at AWS AI Labs and is working on advancing the recommender system for Amazon Personalize. His research interests lie in recommendation foundation models, Bayesian deep learning, large language models (LLMs) and their applications in recommendation.