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).