An AI recommendation system in ASP.NET works by combining user data, machine learning models, and backend APIs to deliver personalized suggestions in real time.
First, you will need data. Without it, AI can not learn from anything. Also, put this data in a structured format.Â
Step 1: Gather and present data from users
First, you will need data. Without it, AI can not learn from anything. Also, put this data in a structured format.
What data to collect?
- What user clicks
- What they look like
- What they buy
Step 2: Gather the data
Raw data is presented messily, so you:
- Remove duplicates
- Convert actions into numbers
- Create a user-item matrix
This helps AI understand user preferences.
Step 3: Develop an AI Model With ML.NET
Now, into the core of the system, we have what a collaborative and filtered approach is.
What it does:
- Finds users with similar behavior
- Recommends what similar users liked
Step 4: Train the Model and Develop .NET Core API
Teach AI using your data, feed it data into the ML model, and learn patterns. Now expose recommendations via API.
Example:
/api/recommendations/{userId}
Step 5: Get what out of the model
When a user visits our app, we pass the userId to the model that then puts forth top products like:
[Mouse, Keyboard, Laptop Bag]
Step 6: Display on UI
Now users present their own display of the Homepage and Product page. Frontend interfaces with our ASP.NET API, which in turn presents the results.
