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 item...