+++
title = "§11 Exercise 4: Recommendation Engine"
priority = 5
status = "done"
ticket_type = "task"
dependencies = []
+++
## §11 Exercise 4 — Recommendation Engine — Stub to fill

File: `edu/src/vector-db.md`, section `### 11. Exercise 4 — Recommendation Engine`

Replace this stub line with the full exercise:
> **Goal:** Implement item-based collaborative filtering using vector similarity. [...] 🚧 Full content tracked in [nbd:e8be9a].

Follow the exercise format from `edu/src/markov.md`.

## Goal

Build an item-based recommendation engine. Store item feature vectors in Turso, then given a target item, find the k most similar items using KNN and exclude the query item from the results.

## Approach

Use hand-crafted 5-dimensional feature vectors for a product catalogue (no fastembed dependency needed — keeps focus on the recommendation logic). Dimensions represent affinity scores for: [electronics, clothing, sports, food, books].

## Catalogue (10 items)

| id | name | embedding |
|---|---|---|
| 1 | "Laptop" | [0.95, 0.0, 0.1, 0.0, 0.2] |
| 2 | "Mechanical Keyboard" | [0.85, 0.0, 0.0, 0.0, 0.1] |
| 3 | "USB-C Hub" | [0.9, 0.0, 0.0, 0.0, 0.0] |
| 4 | "Running Shoes" | [0.0, 0.6, 0.9, 0.0, 0.0] |
| 5 | "Yoga Mat" | [0.0, 0.2, 0.95, 0.0, 0.0] |
| 6 | "Water Bottle" | [0.1, 0.1, 0.7, 0.0, 0.0] |
| 7 | "T-Shirt" | [0.0, 0.95, 0.1, 0.0, 0.0] |
| 8 | "Cookbook" | [0.0, 0.0, 0.0, 0.6, 0.9] |
| 9 | "Protein Bar" | [0.0, 0.0, 0.3, 0.95, 0.0] |
| 10 | "Novel" | [0.0, 0.0, 0.0, 0.1, 0.95] |

## Steps to cover

**Step 1 — Schema.** Table `products (id INTEGER PRIMARY KEY, name TEXT NOT NULL, embedding F32_BLOB(5) NOT NULL)` with a `libsql_vector_idx` HNSW index.

**Step 2 — Insert items.** Same pattern as Exercise 1: format `Vec<f32>` as JSON, `INSERT OR IGNORE`.

**Step 3 — Recommend function.** Write a helper:

```rust
async fn recommend(
    conn: &libsql::Connection,
    item_id: i64,
    k: usize,
) -> Result<Vec<(String, f64)>, Box<dyn std::error::Error>>
```

1. `SELECT vector_extract(embedding) FROM products WHERE id = ?` to get the query item's embedding as a JSON string
2. Pass that JSON string to `vector_top_k` with k+1 (to have room to exclude the query item)
3. JOIN to get product names and `vector_distance_cos` distances
4. Filter out `products.id = item_id`
5. Return the top k `(name, distance)` pairs

**Step 4 — Print recommendations for three items.**
- "Laptop" → expect Mechanical Keyboard, USB-C Hub (electronics cluster)
- "Running Shoes" → expect Yoga Mat, Water Bottle (sports cluster)
- "Cookbook" → expect Novel, Protein Bar (food/books cluster)

Output format: `"Customers who liked Laptop also liked: Mechanical Keyboard (0.023), USB-C Hub (0.041)"`

## Reference solution

Full `main.rs` inside `<details>`. The `recommend` function should be clearly separated from the setup boilerplate. The recommendation query pattern (SELECT embedding → feed as query to vector_top_k) is the key technique to highlight.