django-slopeone

Generates rating predictions based on the Slope One algorithm. The rating predictions can be used for recommendations.

You can check out the following links about the Slope One algorithm:

• http://en.wikipedia.org/wiki/Slope_One
• http://www.daniel-lemire.com/fr/documents/publications/lemiremaclachlan_sdm05.pdf

Note: This project is under development and things will most likely change. Don't use it in production systems.

Installation

You need Django 1.1.

1. Run setup.py install
2. Put slopeone in INSTALLED_APPS
3. Run manage.py syncdb

Usage

1. Add some ratings to the Rating table. You can use the admin for that.
2. Run the manage.py update_calculations command
3. In your view, import slopeone.core.recommend and execute it with the user for which the recommendations should be generated.

The recommend method will return a list of recommendations. The output looks like this:

[(2, 5.0), (1, 3.0)]

Each element is a pair consisting of the recommended object id and the predicted rating. In this case, the predicted rating for item 2 is 5.0 and for item 1 it's 3.0. The list is sorted descending by rating.

Example Project

If you cloned the git repo, you can use the testproject to test this project. The testproject includes 2 users and 3 rated (animal) fixtures. Here are some usage instructions:

(0. You need the slopeone module installed/in your pythonpath.)

1. Run manage.py syncdb in the testproject dir but don't create a superuser.
2. Load the fixtures by executing manage.py loaddata fixtures/*. (username==password for the user fixtures)

The following ratings have been added:

=User | =Cat | =Dog

admin | 5 | 3

arthur | ? | 4

3. Run the manage.py update_calculations command, to compute all required values.

4. To compute the predicted rating for the user arthur and the animal object cat, you can run the manage.py show_recommendations command with the user id 2 as an argument:

   ./manage.py show_recommendations 2
   $ [(2, 6.0), (1, 4.0)]

As you see, the predicted rating for arthur/cat would be 6.0. This is because there is only one other user (admin), and he rated cat with 5 and dog with 3. So the global difference between dog and cat rating is +2. Since arthur rated dog with 4, the prediction will apply the difference of the global cat/dog rating to the value of the dog rating. So that 4+2=6.

The rating for the object with the id 1 is not computed since the user has manually rated that object. Rated items are currently not excluded from the return value of the recommend method.

Known Issues

• Items that were already rated by the user are returned in the recommendations. They should be excluded.
• Ratings don't have a maximum value (to, for example, support ratings from a range from 1-5)
• Although the contenttypes framework is used, django-slopeone currently only supports one content type.