Collaborative Filtering 協同過濾推薦
Released已發布Implement collaborative filtering for recommendations based on user behavior patterns. Use this skill when the user needs to build a recommendation engine from user-item interaction data, find similar users or items, or predict ratings — even if they say 'users who bought this also bought', 'similar users', or 'recommend based on behavior'.
演算法技能：Collaborative Filtering 分析與應用。
Overview概述
Collaborative filtering recommends items based on collective user behavior patterns. User-based CF finds similar users; item-based CF finds similar items. Computes in O(U² × I) for user-based or O(I² × U) for item-based where U=users, I=items.
When to Use使用時機
Trigger conditions:
- Building recommendations from user-item interaction data (ratings, clicks, purchases)
- Finding "users like you also liked" or "frequently bought together" patterns
When NOT to use:
- When you have no interaction data (cold start — use content-based filtering)
- When item features matter more than behavior patterns (use content-based)
Algorithm 演算法
IRON LAW: CF Requires SUFFICIENT Interaction Data
With sparse matrices (< 1% fill rate), similarity computation is
unreliable. Minimum viable: each user has rated 5+ items, each item
has 5+ ratings. Below this, fallback to content-based or popularity.
Phase 1: Input Validation
Load user-item interaction matrix. Check sparsity level and filter users/items below minimum interaction threshold. Gate: Matrix sparsity < 99%, minimum interaction thresholds met.
Phase 2: Core Algorithm
User-based CF:
- Compute pairwise user similarity (cosine or Pearson correlation)
- For target user, find top-K most similar users
- Predict rating: weighted average of similar users' ratings
Item-based CF:
- Compute pairwise item similarity from co-rating patterns
- For target item, find top-K most similar items
- Predict: weighted average of user's ratings on similar items
Phase 3: Verification
Hold out 20% of interactions for testing. Compute RMSE, MAE, or precision@K / recall@K. Gate: RMSE below baseline (global mean predictor).
Phase 4: Output
Return top-N recommendations with predicted scores.
Output Format輸出格式
{
"recommendations": [{"item_id": "123", "predicted_score": 4.2, "similar_items_used": 5}],
"metadata": {"method": "item-based", "similarity": "cosine", "k_neighbors": 20, "sparsity": 0.97}
}
Examples範例
Sample I/O
Input: 5 users × 5 items rating matrix, target: user1, item5 Expected: Predicted rating based on weighted similarity of user1's rated items similar to item5
Edge Cases
| Input | Expected | Why |
|---|---|---|
| New user, no ratings | Cannot recommend | Cold start — fallback to popularity |
| Item rated by all users | Low differentiation | High popularity ≠ personalized match |
| Single shared item | Unreliable similarity | Need multiple co-ratings for stable similarity |
Gotchas注意事項
- Scalability: User-based CF with millions of users is O(U²). Use approximate nearest neighbors (LSH) or switch to item-based CF (item catalog is usually smaller).
- Popularity bias: Popular items have more co-ratings, inflating their similarity scores. Normalize by inverse popularity.
- Implicit vs explicit feedback: Clicks/views (implicit) need different treatment than ratings (explicit). Use confidence weighting for implicit data.
- Similarity metric matters: Cosine similarity ignores rating scale differences; Pearson correlation accounts for user rating biases. Choose based on data characteristics.
- Gray sheep: Users with unusual taste patterns have no similar peers. CF fails for them — consider hybrid approaches.
References參考資料
- For matrix factorization as a scalable alternative, see
references/matrix-factorization.md - For implicit feedback handling, see
references/implicit-feedback.md