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Recommender systems

A recommender system allows you to provide personalized recommendations to users. With this toolkit, you can create a model based on past interaction data and use that model to make recommendations.

Input data

Creating a recommender model typically requires a data set to use for training the model, with columns that contain the user IDs, the item IDs, and (optionally) the ratings. For this example, we use the MovieLense dataset.

import turicreate as tc
actions = tc.SFrame.read_csv('./dataset/ml-20m/ratings.csv')
+--------+---------+--------+------------+
| userId | movieId | rating | timestamp  |
+--------+---------+--------+------------+
|   1    |    2    |  3.5   | 1112486027 |
|   1    |    29   |  3.5   | 1112484676 |
|   1    |    32   |  3.5   | 1112484819 |
|   1    |    47   |  3.5   | 1112484727 |
|   1    |    50   |  3.5   | 1112484580 |
|   1    |   112   |  3.5   | 1094785740 |
|   1    |   151   |  4.0   | 1094785734 |
|   1    |   223   |  4.0   | 1112485573 |
|   1    |   253   |  4.0   | 1112484940 |
|   1    |   260   |  4.0   | 1112484826 |
+--------+---------+--------+------------+

For information on how to load data into an SFrame from other sources, see the chapter on SFrames.

You may have additional data about users or items. For example we might have a dataset of movie metadata.

items = tc.SFrame.read_csv('./dataset/ml-20m/movies.csv')
+---------+---------------------+---------------------+------+
| movieId |        title        |        genres       | year |
+---------+---------------------+---------------------+------+
|    1    |      Toy Story      | [Adventure, Anim... | 1995 |
|    2    |       Jumanji       | [Adventure, Chil... | 1995 |
|    3    |   Grumpier Old Men  |  [Comedy, Romance]  | 1995 |
|    4    |  Waiting to Exhale  | [Comedy, Drama, ... | 1995 |
|    5    | Father of the Br... |       [Comedy]      | 1995 |
+---------+---------------------+---------------------+------+

If you have data like this associated with each item, you can build a model from just this data using the item content recommender. In this case, providing the user and item interaction data at model creation time is optional.

Creating a model

There are a variety of machine learning techniques that can be used to build a recommender model. Turi Create provides a method turicreate.recommender.create that will automatically choose an appropriate model for your data set.

First we create a random split of the data to produce a validation set that can be used to evaluate the model.

training_data, validation_data = tc.recommender.util.random_split_by_user(actions, 'userId', 'movieId')
model = tc.recommender.create(training_data, 'userId', 'movieId')

Now that you have a model, you can make recommendations

results = model.recommend()

Learn more

The following sections provide more information about the recommender model:

  • Using models
    • making recommendations
    • finding similar items and users
    • evaluating the model
    • interactive visualizations
    • saving models
    • and more
  • Choosing a model
    • data you might encounter (implicit or explicit)
    • types of models worth considering (item-based similarity, factorization-based models, and so on).
  • API Docs