SQL plays a key role in recommendation systems for data cleaning, feature engineering, and sample generation. The first step is to clean and organize user behavior data, use DISTINCT or GROUP BY to deduplicate and filter invalid behavior; the second step is to build the user-item interaction matrix, and use PIVOT or CASE WHEN to construct a wide table to support collaborative filtering model; the third step is to offline feature engineering and label generation, and count user portraits and item characteristics through SQL; the fourth step is to build the training samples and label alignment, including the generation of positive and negative samples and feature stitching.

The support of data is indispensable behind the recommendation system, and SQL, as the core tool for processing structured data, plays an important role in the construction of the recommendation engine. Whether it is the sorting of user behavior data, the preparation of feature engineering, or the generation of offline training data, SQL can be efficiently completed.

Cleaning and organizing user behavior data

The first step in the recommendation system is usually to collect and process user behavior data, such as clicks, browsing, purchases, etc. This data often comes from the log system, and the original data may be duplicated, abnormal or missing.

Suggested practices:

• Use DISTINCT or GROUP BY to deduplicate
• Set time range filtering invalid behavior, such as keeping only data for the last 30 days
• Filter outliers, such as the page stays for too long, which may be dirty data.

 SELECT user_id, item_id, COUNT(*) AS click_count
FROM user_clicks
WHERE event_time BETWEEN '2023-01-01' AND '2023-01-31'
GROUP BY user_id, item_id
HAVING COUNT(*) > 1

This type of query can help you find items that users click repeatedly as preliminary data for collaborative filtering.

Build user-item interaction matrix

A commonly used input form in the recommendation system is the user-item interaction matrix. Each row represents a user and each column represents an item. The values can be ratings, clicks, purchases, etc.

Common practices:

• Use PIVOT or CASE WHEN to construct wide tables
• If there are too many items, consider keeping only high-frequency items or using embedded vectors instead

 SELECT user_id,
       SUM(CASE WHEN item_id = 'item_001' THEN 1 ELSE 0 END) AS item_001_clicks,
       SUM(CASE WHEN item_id = 'item_002' THEN 1 ELSE 0 END) AS item_002_clicks
FROM user_clicks
GROUP BY user_id

This structured data can be used directly for some model training based on collaborative filtering.

Offline feature engineering and tag generation

In recommended model training, feature engineering is a very critical link. SQL can be used to generate user portraits, item characteristics, historical behavior statistics, etc.

Common features:

• Historical click-through rate of users
• User preference for a certain type of item
• The popularity trend of items

 -- Calculate the number of clicks per user to different categories SELECT user_id, category_id, COUNT(*) AS click_count
FROM user_clicks
JOIN items ON user_clicks.item_id = items.id
GROUP BY user_id, category_id

This type of feature can be used as input to the model to help the model better understand user interests.

Build training samples align with labels

When training a recommendation system, it is often necessary to align user behavior with the target label, such as predicting whether the user will click on an item.

Key steps:

• Build a positive sample (item that the user clicks)
• Build negative samples (items that the user does not click on, usually require sampling)
• Splicing user features and item features

 -- Construct positive samples SELECT u.user_id, i.item_id, 1 AS label
FROM user_clicks u
JOIN items i ON u.item_id = i.id
WHERE u.event_time > '2023-01-01'

This type of SQL can be used as the basis for training sample generation, and can be further processed in conjunction with the machine learning framework in the future.

SQL functions far more than data query in recommendation systems, it is an important bridge between the original data and the algorithmic model. Mastering SQL skills can help you achieve twice the result with half the effort in the development of recommended systems.

Basically all is it, not complicated but it is easy to ignore details.

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