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

• 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