Imagine browsing an online store and seeing items tailored just for you. That’s the magic of recommendation engines! They analyze your preferences and suggest relevant products, keeping you engaged and boosting sales. This article empowers you to build your own recommendation engine using a powerful trio: .NET Core, MS SQL Server, and Next.js.

Understanding the Building Blocks:

Before diving in, let’s get familiar with the key players:

  • Recommendation Engine: It’s like a smart assistant, recommending products you might like based on your past behavior and similarities with other users.
  • .NET Core: This lightweight, open-source framework is perfect for building modern web applications.
  • MS SQL Server: This popular database management system efficiently stores and retrieves your data.
  • Next.js: This React framework offers flexibility, allowing you to create dynamic and performant web applications using various rendering approaches.

Building Your Recommendation Engine:

Now, let’s break down the essential steps involved:

  1. Collecting and Storing Data:
  • Data Sources: Gather information from various sources: user interactions (clicks, purchases, browsing behavior), product details (category, brand, price), and user profiles (demographics, interests).
  • Data Storage: Utilize MS SQL Server’s power to store this data efficiently. Consider using Entity Framework Core for smooth communication between your .NET Core application and the database.
  1. Choosing the Right Recommendation Algorithm:
  • Content-based Filtering: Recommends similar items based on product attributes (e.g., suggesting similar clothes based on size and color).
  • Collaborative Filtering: Recommends items that users with similar preferences have enjoyed (e.g., suggesting movies users with similar taste have watched).
  • Hybrid Approach: Combine both methods for more comprehensive recommendations.
  1. Implementation in .NET Core:

Develop the recommendation engine logic in your .NET Core backend application. Utilize libraries like Apache Mahout or ML.NET for implementing chosen recommendation algorithms. Connect to MS SQL Server using Entity Framework Core to retrieve and store data required for recommendation calculations.

  1. Integration with Next.js Frontend:
  • Expose the recommendation engine API endpoints from your .NET Core backend.
  • Utilize Next.js API routes or data fetching techniques (like getStaticProps or getServerSideProps) to fetch recommendations from the backend.

Code Example (Simplified .NET Core Backend with Example Recommendation):

Code snippet

public class RecommendationService
    private readonly IMongoDatabase _database; // Replace with appropriate database connection

    public RecommendationService(IMongoDatabase database)
        _database = database;

    public List<Product> GetRecommendations(string userId)
        // Implement recommendation logic using collaborative filtering or other methods
        // Access user data and product information from database using Entity Framework Core
        // Return a list of recommended products

Use code with caution.content_copy

  • Display these personalized recommendations on your frontend using Next.js components and data.

Real-World Use Cases:

  • E-commerce: Recommend similar products based on past purchases, browsing history, and user demographics.
  • Streaming Services: Suggest movies and shows based on what users have watched and their preferences.
  • Music Platforms: Recommend music based on listening history and similar artists enjoyed by other users.

Additional Considerations:

  • Scalability: Ensure your chosen architecture and database can handle anticipated growth in users and data volume.
  • Explainability: Consider how you can explain your recommendations to users, building trust and transparency.
  • Experimentation and Optimization: Continuously test and refine your recommendation engine to improve effectiveness and user satisfaction.


By harnessing the combined power of .NET Core, MS SQL Server, and Next.js, you can craft a robust and scalable recommendation engine that personalizes the user experience and drives business value for your web-based product. Remember, this is a general framework, and specific implementation details may vary based on your unique project needs and chosen technologies.

Additional Resources: