Generative AI has a broad variety of use cases and this is precisely why it is being adopted widely and is rapidly transforming many sectors. However, its application in many areas remains unclear.
We have worked with numerous customers across industries like retail, SaaS, finance and more to determine avenues to generate value with Generative AI. In some circumstances, Generative AI can even augment data analytics capabilities that are generally approached with traditional AI and machine learning technologies.
We will explore how GenAI enhances traditional recommendation engines, and provide an overview of how to build a GenAI recommendation engine with AWS Bedrock and Amazon Personalize.
What is a GenAI Recommendation Engine?
A GenAI recommendation engine is an advanced approach to personalized recommendations that leverages generative artificial intelligence to create tailored suggestions for users. Unlike traditional recommendation engines, which primarily rely on historical data and predefined patterns, GenAI recommendation engines can generate new, context-aware recommendations by understanding and interpreting complex user preferences and behaviors.
These systems work by combining large language models (LLMs) with traditional recommendation algorithms. The GenAI component analyzes user data, preferences, and contextual information to generate personalized content, product descriptions, or even entirely new recommendations that might not exist in the current inventory. This approach allows for more nuanced, creative, and potentially more relevant suggestions to users
Traditional vs. GenAI Recommendation Engines
Traditional recommendation engines rely on analyzing user behavior and item characteristics to predict preferences. They might use algorithms that focus on past interactions, like purchases or views, to suggest similar items or those that other similar users have liked.
However, GenAI introduces a new paradigm by generating content or item features based on understanding the data context. While GenAI shows promising possibilities, it's important to balance the advantages against the costs. In many cases, traditional methods powered by services like Amazon Personalize may still be the best choices due to their scalability and cost-effectiveness. Yet, the integration of GenAI can offer unparalleled advantages in creating more personalized user experiences.
Overview: How to Build a Recommendation Engine
In this post we’re focused on how to build a recommendation engine using AWS Bedrock and Amazon Personalize.
We’ll pick apart our process in a case study later on in the post. But let’s start with an overview of the process.
Prerequisites
- Active AWS account
- IAM roles/permissions for Amazon Personalize and Bedrock
- Prepared data (user, item, and interaction data) in Amazon S3
- Access to Bedrock foundation models (request if needed)
- Amazon Personalize datasets and dataset group created
- (Optional) Vector store set up for Bedrock knowledge base
- Security/compliance settings configured as required
Step 1: Import and Prepare Your Data
Gather and import user, item, and interaction data into Amazon Personalize. While only interaction data is required, including user and item metadata will improve recommendation quality.
Step 2: Train a Recommender in Amazon Personalize
Use Amazon Personalize to train a recommender, such as the “Top picks for you” model. The service automatically configures the best model for your use case and filters out content users have already interacted with.
Step 3: Retrieve Personalized Recommendations
Query the trained recommender using the Amazon Personalize Runtime API, providing user IDs to receive top recommendations for each user.
Step 4: Create a Personalized Prompt
Combine the recommended items and user demographic data with a predefined prompt template. This template will guide the generative model in crafting personalized content (e.g., an email or marketing message).
Step 5: Generate Content with AWS Bedrock
Send the enhanced prompt to a foundation model in Amazon Bedrock via its API. Bedrock will generate personalized outbound communication based on the recommendations and user data.
Step 6: Deliver the Personalized Content
Use your preferred channel (email, app notification, etc.) to send the generated personalized message to each user.
Use cases for GenAI recommendation engines
GenAI recommendation engines offer innovative solutions across various industries. Here are some top use cases:
Product recommendations
In e-commerce, GenAI can create highly personalized product recommendations by generating detailed product descriptions tailored to individual user preferences. It can also suggest product bundles or combinations that may not exist in the catalog but are likely to appeal to specific customers based on their browsing and purchase history.
Personalized content
For content platforms like news sites or blogs, GenAI can generate personalized article summaries, headlines, or even entire articles based on a user's reading history and interests. This can significantly enhance user engagement and time spent on the platform.
TV and movie recommendations
In the streaming entertainment industry, GenAI recommendation engines can significantly enhance the user experience. These systems can analyze a user's viewing history, preferences, and even contextual factors like time of day or current events to generate highly personalized recommendations.
For example, a GenAI system could create custom content descriptions that highlight aspects of a show or movie most likely to appeal to a specific user. It might also suggest unique combinations of genres or themes based on a user's eclectic tastes, potentially recommending content that traditional algorithms might overlook.
Location-based recommendations
In travel and hospitality, GenAI can generate personalized itineraries or activity recommendations based on a user's preferences, past travels, and current location. It can create detailed descriptions of attractions or restaurants that cater to the user's specific interests and dietary requirements.
Types of GenAI Recommendation Engines
- Content-Based GenAI Recommenders: These systems analyze item features and user preferences to generate new, tailored item descriptions or even entirely new items that match user interests. Unlike traditional content-based systems, GenAI can create more nuanced and context-aware descriptions.
- Collaborative Filtering with GenAI: While traditional collaborative filtering relies on user-item interactions, GenAI-enhanced versions can generate synthetic users or items to fill in data gaps, improving recommendations for new users or items with limited data.
- Hybrid GenAI Recommenders: These systems combine multiple approaches, using GenAI to enhance both content-based and collaborative filtering methods. They can generate new features for items, create synthetic user profiles, and even propose novel items that don't exist in the current inventory.
- Context-Aware GenAI Recommenders: These systems go beyond traditional context-aware recommenders by generating situational content. For example, they might create personalized product descriptions that take into account the user's current weather, location, or even mood.
Building a GenAI Recommendation Engine With AWS Bedrock and Amazon Personalize
Using GenAI spikes the cost and diminishes the performance when used as the recommendation engine. We can either get data for each customer to personalize the promotions and prompt it to get a recommendation. Or we can get a chunk of data, but then we lose the personalization power. Both performance and cost are not reasonable.
Therefore, using Amazon Personalize, the fully managed machine learning service that uses the same data to generate item recommendations, is much more suitable. We combine it with GenAI by getting Personalize's recommendations and crafting content to present to the client, enriching the personalization experience. A high-level solution architecture diagram for using Personalize with GenAI capabilities is shown below.
Danilo Figueiredo
Sankalp Prabhakar
