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75 lines
3.0 KiB
Markdown
75 lines
3.0 KiB
Markdown
---
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# edu-dgfl
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title: '§11 Exercise 4: Recommendation Engine'
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status: completed
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type: task
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priority: normal
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created_at: 2026-03-10T23:30:02Z
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updated_at: 2026-03-10T23:30:02Z
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---
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## §11 Exercise 4 — Recommendation Engine — Stub to fill
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File: `edu/src/vector-db.md`, section `### 11. Exercise 4 — Recommendation Engine`
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Replace this stub line with the full exercise:
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> **Goal:** Implement item-based collaborative filtering using vector similarity. [...] 🚧 Full content tracked in [nbd:e8be9a].
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Follow the exercise format from `edu/src/markov.md`.
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## Goal
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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.
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## Approach
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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].
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## Catalogue (10 items)
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| id | name | embedding |
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|---|---|---|
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| 1 | "Laptop" | [0.95, 0.0, 0.1, 0.0, 0.2] |
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| 2 | "Mechanical Keyboard" | [0.85, 0.0, 0.0, 0.0, 0.1] |
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| 3 | "USB-C Hub" | [0.9, 0.0, 0.0, 0.0, 0.0] |
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| 4 | "Running Shoes" | [0.0, 0.6, 0.9, 0.0, 0.0] |
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| 5 | "Yoga Mat" | [0.0, 0.2, 0.95, 0.0, 0.0] |
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| 6 | "Water Bottle" | [0.1, 0.1, 0.7, 0.0, 0.0] |
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| 7 | "T-Shirt" | [0.0, 0.95, 0.1, 0.0, 0.0] |
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| 8 | "Cookbook" | [0.0, 0.0, 0.0, 0.6, 0.9] |
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| 9 | "Protein Bar" | [0.0, 0.0, 0.3, 0.95, 0.0] |
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| 10 | "Novel" | [0.0, 0.0, 0.0, 0.1, 0.95] |
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## Steps to cover
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**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.
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**Step 2 — Insert items.** Same pattern as Exercise 1: format `Vec<f32>` as JSON, `INSERT OR IGNORE`.
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**Step 3 — Recommend function.** Write a helper:
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```rust
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async fn recommend(
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conn: &libsql::Connection,
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item_id: i64,
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k: usize,
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) -> Result<Vec<(String, f64)>, Box<dyn std::error::Error>>
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```
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1. `SELECT vector_extract(embedding) FROM products WHERE id = ?` to get the query item's embedding as a JSON string
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2. Pass that JSON string to `vector_top_k` with k+1 (to have room to exclude the query item)
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3. JOIN to get product names and `vector_distance_cos` distances
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4. Filter out `products.id = item_id`
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5. Return the top k `(name, distance)` pairs
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**Step 4 — Print recommendations for three items.**
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- "Laptop" → expect Mechanical Keyboard, USB-C Hub (electronics cluster)
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- "Running Shoes" → expect Yoga Mat, Water Bottle (sports cluster)
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- "Cookbook" → expect Novel, Protein Bar (food/books cluster)
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Output format: `"Customers who liked Laptop also liked: Mechanical Keyboard (0.023), USB-C Hub (0.041)"`
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## Reference solution
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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.
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