/algo-rec-cf

name: "\"algo-rec-cf\""
description: "\"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'.\"."
allowed-tools: Read, Glob, Grep

Collaborative Filtering

Overview

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

**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

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:** 1. Compute pairwise user similarity (cosine or Pearson correlation) 2. For target user, find top-K most similar users 3. Predict rating: weighted average of similar users' ratings

**Item-based CF:** 1. Compute pairwise item similarity from co-rating patterns 2. For target item, find top-K most similar items 3. 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

{
  "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

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

• **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

• For matrix factorization as a scalable alternative, see `references/matrix-factorization.md`
• For implicit feedback handling, see `references/implicit-feedback.md`