Azure Intelligent Recommendations

  • Author: Ronald Fung

  • Creation Date: 1 June 2023

  • Next Modified Date: 1 June 2024

A. Introduction

Intelligent Recommendations democratizes AI and machine learning recommendations through a codeless experience powered by the same technology that fuels Xbox, Microsoft 365, and Microsoft Azure. Businesses can now provide relevant discovery for customers with this new, innovative AI for personalization and recommendations.

Intelligent Recommendations provides personalized product recommendations and telemetry insights using modern machine-learning algorithms. These recommendations and insights help you:

  • Significantly improve catalog navigation and item discovery.

  • Create upsell and cross-sell opportunities.

  • Improve shoppers’ experiences and product usability.

Capabilities

Intelligent Recommendations helps companies drive better engagement, conversion, revenue, and customer satisfaction. Intelligent Recommendations is a general purpose service that offers one-of-a-kind, patented capabilities. It effectively drives desired outcomes out of the box such as “shop similar looks,” “shop by description,” “real time,” “session based”, Item based recommendations that can combine User interactions and Item Metadata. Businesses can promote and personalize any content type, such as sellable products, consumable media, documents, videos, and more.

Intelligent Recommendations provides the following capabilities for businesses:

  • Built-in world-class AI-ML delivers amazing, personalized results within minutes. Provide insightful, personalized, tailored, and more engaging customer experiences from existing user behavior data or item metadata, to create measurable lift in any business.

  • Easy to integrate and extend in any ecosystem and experience. Use codeless tooling guided by business intuition to easily try, build, and deliver any model customization. When paired with extensible APIs, this capability allows seamless integration into any ecosystem.

  • Trustworthy software service at scale. Microsoft is democratizing the machine-learning expertise, compliant platform, and high-scale capabilities, so businesses can focus on the next horizon of growth and innovation.

  • Win over customers with delightful discovery. Power hyper-relevant suggestions for any customer or product on Azure, ensuring a personalized journey every time a customer interacts with your business.

  • Highly composable, easily extensible. Highly adaptable to custom business scenarios and logic, based on input data and algorithm choice.

B. How is it used at Seagen

As a biopharma research company using Microsoft Azure, you can use Azure Intelligent Recommendations to provide personalized recommendations to your users based on their preferences and behavior. Here are some ways you can use Azure Intelligent Recommendations:

  1. Personalized product recommendations: Azure Intelligent Recommendations can analyze user behavior, such as purchases and searches, to provide personalized product recommendations to your users, helping them find the products that are most relevant to their needs.

  2. Content recommendations: Azure Intelligent Recommendations can analyze user behavior to provide personalized content recommendations, such as articles or research papers, based on their interests and previous interactions.

  3. Search recommendations: Azure Intelligent Recommendations can provide search recommendations to help users find the most relevant search results based on their search history and behavior.

  4. Cross-sell and upsell recommendations: Azure Intelligent Recommendations can provide cross-sell and upsell recommendations to help users discover complementary products or upgrade to higher-end products.

  5. Recommendations for clinical trials: Azure Intelligent Recommendations can analyze user behavior to provide personalized recommendations for clinical trials that may be of interest to users based on their medical history and preferences.

  6. Integration with other Azure services: Azure Intelligent Recommendations can integrate with other Azure services, such as Azure Machine Learning and Azure Cognitive Services, to provide a more comprehensive and customized recommendation engine.

Overall, Azure Intelligent Recommendations provides a powerful and flexible tool for providing personalized recommendations to your users based on their preferences and behavior. By leveraging the scalability, security, and performance of the service, you can improve the user experience and increase engagement with your products and services.

C. Features

Azure Intelligent Recommendations is a cloud-based recommendation service that uses machine learning algorithms to provide personalized recommendations to users based on their behavior and preferences. Here are some of the key features of Azure Intelligent Recommendations:

  1. Personalized recommendations: Azure Intelligent Recommendations provides personalized recommendations to users based on their behavior and preferences, increasing engagement and satisfaction with your products and services.

  2. Machine learning algorithms: Azure Intelligent Recommendations uses machine learning algorithms, such as collaborative filtering and matrix factorization, to analyze user behavior and provide personalized recommendations.

  3. Integration with Azure services: Azure Intelligent Recommendations integrates with other Azure services, such as Azure Machine Learning and Azure Cognitive Services, to provide a more comprehensive and customized recommendation engine.

  4. Real-time recommendations: Azure Intelligent Recommendations can provide real-time recommendations to users, allowing them to discover new products and services as they interact with your website or application.

  5. Customizable recommendations: Azure Intelligent Recommendations can be customized to meet the specific needs of your business, allowing you to configure recommendation algorithms and parameters to provide the best recommendations to your users.

  6. Scalable: Azure Intelligent Recommendations is scalable, allowing it to handle large volumes of data and provide recommendations to millions of users.

  7. Monitoring and reporting: Azure Intelligent Recommendations provides monitoring and reporting capabilities, allowing you to track recommendation performance and identify areas for improvement.

  8. Security: Azure Intelligent Recommendations provides built-in security features, ensuring that user data is properly secured and protected.

Overall, Azure Intelligent Recommendations provides a powerful and flexible tool for providing personalized recommendations to users based on their behavior and preferences. By leveraging the scalability, security, and performance of the service, you can improve the user experience and increase engagement with your products and services.

D. Where Implemented

LeanIX

E. How it is tested

Testing Azure Intelligent Recommendations involves verifying that the recommendation engine is providing accurate and relevant recommendations to users based on their behavior and preferences. Here are some steps you can take to test Azure Intelligent Recommendations:

  1. Verify configuration: Verify that Azure Intelligent Recommendations is properly configured and integrated with your Azure account and resources.

  2. Test recommendation accuracy: Test Azure Intelligent Recommendations by analyzing the recommendations provided to users and verifying that they are accurate and relevant based on user behavior and preferences.

  3. Test recommendation diversity: Test Azure Intelligent Recommendations by analyzing the diversity of the recommendations provided to users and verifying that they are not too similar or redundant.

  4. Test recommendation performance: Test Azure Intelligent Recommendations by measuring the performance of the recommendation engine, such as the speed of recommendations and the time to update recommendations based on user behavior.

  5. Test recommendation customization: Test Azure Intelligent Recommendations by customizing the recommendation algorithms and parameters and verifying that they provide better recommendations based on your specific business needs.

  6. Test scalability: Test the scalability of Azure Intelligent Recommendations by analyzing the performance of the recommendation engine when handling large volumes of data and providing recommendations to millions of users.

  7. Test monitoring and reporting: Test the monitoring and reporting capabilities of Azure Intelligent Recommendations by tracking recommendation performance and identifying areas for improvement.

Overall, testing Azure Intelligent Recommendations involves verifying that the recommendation engine is providing accurate and relevant recommendations to users, and that it is effectively integrated into your existing workflows and processes. By testing Azure Intelligent Recommendations, you can ensure that you are effectively using the service to provide personalized recommendations to your users, and that you are benefiting from the scalability, security, and performance it provides.

F. 2023 Roadmap

????

G. 2024 Roadmap

????

H. Known Issues

Like any software or service, there may be known issues or limitations with Azure Intelligent Recommendations that users should be aware of. Here are some of the known issues with Azure Intelligent Recommendations:

  1. Limited customization: Azure Intelligent Recommendations has limited customization options, which can limit the ability of users to configure the service to their specific needs.

  2. Limited support for certain recommendation algorithms: Azure Intelligent Recommendations may not support all recommendation algorithms, which can limit the ability of users to perform certain types of recommendation analysis.

  3. Limited data sources: Azure Intelligent Recommendations may have limited data sources, which can limit the ability of users to provide accurate and relevant recommendations to their users.

  4. Limited monitoring and logging: Azure Intelligent Recommendations has limited monitoring and logging capabilities, which can limit the ability of users to monitor and troubleshoot recommendation performance.

  5. Cost: Azure Intelligent Recommendations can be expensive for users with limited budgets, particularly if they need to process large volumes of data or use the service frequently.

  6. Security and compliance concerns: Users must ensure that they are properly securing and protecting user data when using Azure Intelligent Recommendations, particularly when processing data with sensitive data or data subject to regulatory compliance requirements.

Overall, while Azure Intelligent Recommendations offers a powerful and flexible tool for providing personalized recommendations to users, users must be aware of these known issues and take steps to mitigate their impact. This may include carefully configuring the service to meet the specific needs of their data, carefully monitoring the performance and cost of the service to ensure that it is a good fit for their data requirements, and carefully integrating the service into their existing workflows to ensure that it is effectively utilized. By taking these steps, users can ensure that they are effectively using Azure Intelligent Recommendations to provide personalized recommendations to their users, and that they are benefiting from the scalability, security, and performance it provides.

[x] Reviewed by Enterprise Architecture

[x] Reviewed by Application Development

[x] Reviewed by Data Architecture