Content-Based Filtering
Content-based filtering recommends items similar to what a user previously liked — analyzing item features (genre, keywords, attributes) to suggest similar items, enabling personalized recommendations even for new items without user interaction history.
What Is Content-Based Filtering?
- Definition: Recommend items similar to user's past preferences.
- Method: Match item features to user profile.
- Data: Item attributes, user interaction history.
- Principle: If you liked X, you'll like similar items.
How It Works
1. Item Representation: Extract features (genre, keywords, actors, ingredients, specifications).
2. User Profile: Build profile from items user liked (aggregate features).
3. Similarity Matching: Find items similar to user profile.
4. Ranking: Score and rank candidate items.
Feature Types
Structured: Genre, price, size, color, brand, category.
Text: Descriptions, reviews, tags, keywords.
Audio/Visual: Image features, audio features, video content.
Metadata: Author, director, artist, publisher, release date.
Similarity Measures
Cosine Similarity: Angle between feature vectors.
Euclidean Distance: Geometric distance in feature space.
Jaccard Similarity: Overlap of categorical features.
TF-IDF: Text similarity based on term importance.
Advantages
- No Cold Start for Items: New items can be recommended immediately.
- Transparency: Explainable ("Recommended because you liked X").
- User Independence: Doesn't need other users' data.
- Niche Items: Can recommend unpopular items if features match.
Limitations
Limited Diversity: Only recommends similar items (filter bubble).
Feature Engineering: Requires good item features.
New User Cold Start: Still need user history.
Overspecialization: Can't discover different types of items.
No Quality Signal: Doesn't know if similar items are actually good.
Applications
- News: Recommend articles similar to what you read.
- Movies: "If you liked this movie, try these similar films."
- Music: Recommend songs with similar audio features.
- E-Commerce: Products with similar specifications.
- Jobs: Positions matching your skills and experience.
Tools: scikit-learn (TF-IDF, cosine similarity), Gensim (doc2vec), sentence-transformers (embeddings).