charlieviettq 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
- Represent each item as a feature vector
- Build user profile: weighted centroid of interacted item vectors (weight by recency, rating, or engagement)
- Compute similarity between user profile and all candidate items (cosine similarity)
- 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}
}
Examples
Sample I/O
Input: User watched 5 sci-fi movies, 2 documentaries. Candidate: new sci-fi movie. Expected: High score (~0.8+) due to genre match with dominant preference.
Edge Cases
| Input | Expected | Why |
|---|---|---|
| New user, no history | Cannot build profile | New-user cold start — use popularity |
| All items same features | Equal scores | No differentiation possible |
| User with diverse history | Moderate scores for all | Profile averages dilute signal |
Gotchas
- Feature quality is everything: Garbage features → garbage recommendations. Invest in feature engineering.
- Filter bubble: Users get increasingly narrow recommendations. Inject diversity by mixing in exploration items.
- Profile drift: User preferences change over time. Apply temporal decay to older interactions.
- Feature sparsity: Items with few features produce unreliable similarity. Set a minimum feature count threshold.
- Over-specialization: A user who rated one jazz album highly shouldn't get ALL jazz. Weight by interaction count, not just rating.
References
- For hybrid approaches combining content and CF, see
references/hybrid-strategies.md - For text-based feature extraction techniques, see
references/feature-extraction.md