Content-Based Filtering — Complete Guide
Content-Based Filtering — Complete Guide: free step-by-step lesson with examples, common mistakes, and interview tips — part of ML.NET Tutorial on Toolliyo Academy.
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ML.NET Tutorial · Lesson 53 of 100
Content-Based Filtering
Foundations ✓ → Models ✓ → NLP & advanced → MLOps
NLP & advanced · 3 — Recs, text, ONNX · ~10 min · Module 6: Recommendation Systems
What is this?
Content-based filtering matches item features (category, tags, price band) to a user profile built from past likes.
Why should you care?
AIPredict cold-start products have no clicks yet but do have catalog attributes.
See it live — copy this example
Use a .NET console or Web API project with Microsoft.ML. Run dotnet run after pasting.
var pipe = ml.Transforms.Categorical.OneHotEncoding("Cat", "Category")
.Append(ml.Transforms.Concatenate("Features", "Cat", "PriceBand", "BrandId"))
.Append(ml.MulticlassClassification.Trainers.SdcaMaximumEntropy());
var model = pipe.Fit(userLabeledItems);
var scored = ml.Model.CreatePredictionEngine<ItemRow, ItemScore>(model)
.Predict(new ItemRow { Category = "Electronics", PriceBand = 2, BrandId = 7 });What happened?
- Train on items the user engaged with; score catalog rows by feature similarity.
- Combine with CF in hybrid systems.
Practice next
- One-hot Category.
- Concatenate price/brand.
- Score unrated SKUs for user.
- Add TF-IDF on title text.
- Blend content score with CF score.
Remember
Item features drive match. Good for cold items. Hybrid with CF later.
AIPredict new SKU launch
Fresh gadget has zero clicks but category match.
Outcome: Still surfaces to electronics buyers.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
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