The popularity recommender is built using Polars to create a simple but fast popularity recommender, recommending items based on global popularity and personal past interaction. It identifies the most popular items from the entire dataset and excludes any items a user has already interacted with. Its straightforward methodology makes it an excellent benchmark for comparing more sophisticated recommendation engines. The ideal use cases for this handler include analyzing ecommerce rating data, web page browsing data, or past purchase data for serving users recommendations.

As the current implementations stand, the input data should be a table containing user-item interaction data:

+---------+---------+--------+
| user_id | item_id | rating |
+---------+---------+--------+
| 1       | 2       | 4      |
| 1       | 3       | 7      |
+---------+---------+--------+

Please note that at the moment this integrations does not support DESCRIBE and FINETUNE features.

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Example

Before creating a popularity recommender model, we need to create an ML engine.

CREATE ML_ENGINE popularity_recommender
FROM popularity_recommender;

You can verify it by running SHOW ML_ENGINES.

Now let’s create a popularity recommender model specifying the necessary input parameters.

CREATE MODEL pop_rec_demo
FROM mysql_demo_db (SELECT * FROM movie_lens_ratings)
PREDICT movieId
USING
  engine = 'popularity_recommender',
  item_id = 'movieId',
  user_id = 'userId',
  n_recommendations = 10;

The required parameters include the following:

  • The item_id parameter that stores items to be recommended; here, these are movies.
  • The user_id parameter that stores users to whom items are recommended.
  • The n_recommendations parameter stores the number of recommendations to be returned.

Here is how to connect the mysql_demo_db used for training the model:

CREATE DATABASE mysql_demo_db
WITH ENGINE = 'mysql',
PARAMETERS = {
    "user": "user",
    "password": "MindsDBUser123!",
    "host": "db-demo-data.cwoyhfn6bzs0.us-east-1.rds.amazonaws.com",
    "port": "3306",
    "database": "public"
};

Here is how to get recommendations per user based on the global most popular items:

SELECT b.*
FROM mysql_demo_db.movie_lens_ratings AS a
JOIN pop_rec_demo AS b;

And here is how to get recommendations for specific users based on popularity:

SELECT b.*
FROM mysql_demo_db.movie_lens_ratings AS a
JOIN pop_rec_demo AS b
WHERE a.userId in (215,216);