"algo-rec-content"

Content-Based Recommendation

Overview

Content-based filtering recommends items whose features match the user's preference profile, built from their interaction history. Computes in O(I × F) per user where I=items, F=features. Solves new-item cold start since items only need features, not interaction history.

When to Use

Trigger conditions:

• Recommending based on item attributes (genre, category, keywords, price range)
• New item cold start: items have features but no interaction data yet
• When user privacy requires no cross-user data sharing

When NOT to use:

• When serendipity matters (content-based creates filter bubbles)
• When item features are unavailable or uninformative (use CF instead)

Algorithm

IRON LAW: Content-Based Can Only Recommend SIMILAR Items
It cannot discover unexpected interests (filter bubble problem).
Users who only interact with action movies will only get action
movie recommendations — even if they'd love a documentary.

Phase 1: Input Validation

Extract item feature vectors (TF-IDF for text, one-hot for categories, numerical for attributes). Build user profile from weighted item features of interacted items. Gate: Item features extracted, user profile vector built.

Phase 2: Core Algorithm

1. Represent each item as a feature vector
2. Build user profile: weighted centroid of interacted item vectors (weight by recency, rating, or engagement)
3. Compute similarity between user profile and all candidate items (cosine similarity)
4. Rank by similarity score, exclude already-interacted items

Phase 3: Verification

Evaluate: does the recommendation list reflect the user's demonstrated preferences? Check diversity metrics. Gate: Recommendations are topically aligned with user history.

Phase 4: Output

Return ranked recommendations with feature-level explanations.

Output Format

{
  "recommendations": [{"item_id": "456", "score": 0.87, "matching_features": ["genre:thriller", "director:Nolan"]}],
  "metadata": {"method": "content-based", "features_used": 15, "profile_items": 30}
}