Recommender Systems · GitBook (original) (raw)
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 MovieLens 20M dataset.1
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 methodturicreate.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
- Saving and loading models
- Choosing a model
- Data you might encounter (implicit or explicit)
- Types of models worth considering (item-based similarity, factorization-based models, and so on).
- Deployment to Core ML
- Export to Core ML, drag and drop in an iOS application
- API Docs