Content-Based Filtering

What is Content-Based Filtering?

Content-based filtering builds recommendations using item features (titles, attributes, specs, images, embeddings) rather than crowd behavior. It scores similarity between items and suggests those most like a shopper’s current or past interests.

How It Works (quick)

• Feature extraction: Text (TF-IDF/BM25, embeddings), attributes (brand, color, price), images (vision embeddings).
• Vectorize & normalize: Create comparable vectors; scale/standardize numeric fields.
• Similarity search: Cosine/Euclidean or ANN (HNSW/IVF) to find nearest items.
• Context mix: Blend with filters (size in stock, price band), business rules, and diversity.
• Real-time: Update short-term profiles from session activity (recent views/clicks).

Why it Matters in E-commerce

• Cold start for items: New SKUs can be recommended immediately via content.
• Explainability: “Because it’s similar in material/fit/brand.”
• Control: Merchandisers can steer with attributes and rules.

Best Practices

• Use hybrid: content-based + collaborative signals for breadth.
• Enforce diversity and availability (don’t recommend out-of-stock).
• Segment by task (similar on PDP vs discovery on category/collection).
• Monitor coverage, novelty, CTR/ATC, and returns rate.
• Refresh embeddings when catalog or taxonomy changes.

Challenges

• Overspecialization: Echo-chamber of “more of the same.”
• Sparse/dirty data: Weak titles/specs hurt quality—invest in enrichment.
• Price/size mismatch: Add constraints so “similar” also fits the shopper.

Examples

• PDP “Similar items” using vision + text embeddings.
• “Complete the look” via attribute complements (style, color palette).
• New arrival bootstrapped from brand/material/use-case features.

Summary

Content-based filtering turns product features into vectors to recommend look-alikes with strong control and instant coverage for new items. Combine with collaborative signals and guardrails for availability, price, and diversity.