You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
71 lines
2.9 KiB
Markdown
71 lines
2.9 KiB
Markdown
+++
|
|
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. |