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As a Cloud Data Engineering Manager at Caylent, Danilo Figueiredo has been leading several different engagements in the data realm, from database migration to generative AI, ensuring they align with Caylent's standards. He also mentors architects and engineers and collaborates closely with Project Managers during the projects and with the pre-sales team during the proposal phase. In his free time, Danilo enjoys engaging in group activities with friends and family, such as games, travel, and culinary experiences, drawing energy from these shared moments.
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Sankalp is an ML Architect with a passion for building impactful machine learning solutions on AWS. He focuses on creating scalable, robust models that drive business growth and enhance operational efficiency across various industries.
View 's articlesGenerative 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 systems, and provide an overview of how to build a GenAI recommendation system with AWS Bedrock and Amazon Personalize.
A GenAI recommendation system is an advanced approach to personalized recommendations that leverages generative artificial intelligence to create tailored suggestions for users. Unlike traditional recommendation systems, which primarily rely on historical data and predefined patterns, GenAI recommendation systems 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 recommendation systems 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.
GenAI recommendation systems offer innovative solutions across various industries. Here are some top use cases:
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.
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.
In the streaming entertainment industry, GenAI recommendation systems 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.
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.
Using GenAI spikes the cost and diminishes the performance when used as the recommendation system. 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.
Amazon Personalize is a fully managed machine learning service provided by Amazon Web Services (AWS). It enables developers to create and deploy personalized recommendations for applications without requiring extensive machine learning expertise.
Amazon Personalize is used to build recommendation systems for various applications, including:
The service uses machine learning algorithms to analyze user behavior data and generate personalized recommendations in real-time. It can handle various types of data, including user interactions, item metadata, and user attributes, to create tailored experiences for each user.
Amazon Personalize now offers the Content Generator, a Generative AI feature designed to elevate traditional suggestions into dynamic, thematic narratives. This enhancement transforms traditional recommendations into captivating themed experiences that significantly boost user engagement and sales.
These themes can be particularly effective in promotional contexts, such as a merchandise sales campaign themed ‘Summer Adventures’ for outdoor gear and accessories, enhancing user engagement and improving the specificity of promotions.
AWS Bedrock is a fully managed service that provides access to high-performance foundation models (FMs) from leading AI companies through a single API. It offers a comprehensive suite of tools and capabilities for building and scaling generative AI applications.
AWS Bedrock is used for:
By integrating AWS Bedrock with other AWS services, developers can create powerful, scalable, and secure generative AI applications that leverage the latest advancements in language models and AI technology.
The architecture for integrating Amazon Personalize with GenAI via AWS Bedrock involves several key components working in harmony:
1. Data Collection and Processing: The first step is gathering user interaction data, item metadata, and other relevant information. This data serves as the foundation for training the recommendation models. Here, you can use services like Amazon S3 for storage, and AWS Glue for data transformation.
2. Model Training with Amazon Personalize: Amazon Personalize allows you to quickly train personalized recommendation models. It automatically optimizes the models based on the provided data, ensuring that the recommendations improve over time.
3. Feature Enhancement with AWS Bedrock: Here, GenAI comes into play. We can use AWS Bedrock to enhance the item features or generate new user content. For instance, you can enrich the dataset for Amazon Personalize by generating synthetic user reviews based on product features or creating detailed item descriptions with aspects users care about.
4. Integration and Deployment: We can then integrate the enriched recommendations into the application. This can be done through APIs, ensuring seamless delivery of personalized recommendations to end-users.
5. Feedback Loop: It is important to establish a continuous feedback mechanism to monitor user interactions with promotions. We can feed this data back into Amazon Personalize to refine and optimize the models, ensuring that the promotions remain effective and user-centric over time.
Let’s look at a project we conducted with a confidential client, in which we developed a proof of concept (PoC) to test the use of GenAI in recommendation systems. Spoiler alert: the concept was actually unproven, and surprisingly, that was great news.
In the current digital marketplace, the demand for personalized customer experiences has never been higher. So, the client wanted to adopt GenAI as the core of their recommendation platform to find new data patterns, improve recommendation quality, and develop new product offerings, as traditional promotional strategies often lack the targeted engagement necessary for optimizing conversion rates. This gap between customer expectations and the actual shopping experience highlights a critical need for innovation in how promotions are crafted and delivered.
The objective was to leverage AI to enhance the personalization of promotional content. By doing so, the client aimed to:
To achieve the business objectives given the context above, we started a PoC to personalize promotions for each client. The PoC goals were:
We relied on the following datasets:
Also, we used pre-trained LLMs from Amazon Bedrock, enabling us to jumpstart the personalization process. The development was divided into five phases:
The initial phase involved filtering data, selecting relevant columns, and merging tables to create a unified dataset that was ready for analysis and model training.
Amazon S3 hosted the compressed CSV files containing the data tables for customers, visits, transactions, and promotions, while SageMaker Studio Notebook accessed the data from S3, performed necessary preprocessing, and prepared the data for prompt engineering.
We added context using demographics and past transaction data, enriching the model's ability to generate personalized content.
We re-coded demographics to ensure the model would interpret them accurately and converted the aggregated data from the previous step into a natural language format that Claude v2.1 large language model (LLM) could process. Then, the LLM generated the top-k recommendations.
We set up the Claude v2.1 model in the SageMaker AI environment via the Bedrock API, to run inferences using the prompt engineered in the previous step. Utilizing Claude v2.1, we did multi-step queries to dive deep into customer individuality.
Finally, we had a model inference feeding the formatted customer data into the LLM to generate promotion recommendations.
For example, the model generates: "For customer 123, offer a 20-off promo code which gives them a 20% discount on electronics, valid for the next two weeks."
Then, we post-process it, converting the model's natural language output into a structured format for practical use. For example, parsing the model's text output to extract promotion details and map them to existing offer IDs in the database.
The final step is setting up metrics to measure the effectiveness of the recommendations (e.g., conversion rate, customer engagement, ROI, etc). We recommended testing and Iterating. This means, running tests with a control group and the recommended promotions to evaluate the performance. Based on the results, refine the model.
We concluded that using GenAI for recommendation systems is costly at the production scale and has limited context processing capabilities.
However, despite disproving the initial concept, we could achieve both the PoC goals by providing the client with an alternative architecture and validating that GenAI can indeed optimize personalization with the appropriate approach.
Through a small-scale implementation, we were able to pivot the architecture towards a more suitable and scalable approach, thereby avoiding investment in an unsustainable solution.
Our final suggestion was to combine GenAI while relying on Amazon Personalize for recommendation infrastructure to overcome the aforementioned challenges. And we will describe that below.
Are you exploring ways to take advantage of Analytical or Generative AI in your organization? Partnered with AWS, Caylent's data experts have extensively helped organizations implement AI solutions with a focus on strategies that generate real ROI. Get in touch with our team to learn how we can accelerate your AI endeavors.
Figure 2: Recommended architecture for recommendation systems
Figure 1: PoC Architecture
Danilo Figueiredo
Sankalp Prabhakar