Recommendation Algorithms

A Recommendation Algorithm is a subclass of recommends.algorithms.base.BaseAlgorithm that implements methods for calculating similarities and recommendations.

Subclasses must implement this methods:

• calculate_similarities(self, vote_list)

  Must return an dict of similarities for every object:

  Accepts a list of votes with the following schema:

  [
      ("<user1>", "<object_identifier1>", <score>),
      ("<user1>", "<object_identifier2>", <score>),
  ]
  

  Output must be a dictionary with the following schema:

  [
      ("<object_identifier1>", [
                      (<related_object_identifier2>, <score>),
                      (<related_object_identifier3>, <score>),
      ]),
      ("<object_identifier2>", [
                      (<related_object_identifier2>, <score>),
                      (<related_object_identifier3>, <score>),
      ]),
  ]
  

• calculate_recommendations(self, vote_list, itemMatch)

  Returns a list of recommendations:

  [
      (<user1>, [
          ("<object_identifier1>", <score>),
          ("<object_identifier2>", <score>),
      ]),
      (<user2>, [
          ("<object_identifier1>", <score>),
          ("<object_identifier2>", <score>),
      ]),
  ]
  

NaiveAlgorithm

This class implement a basic algorithm (adapted from: Segaran, T: Programming Collective Intelligence) that doesn’t require any dependency at the expenses of performances.

Properties

• similarity

  A callable that determines the similiarity between two elements.

  Functions for Euclidean Distance and Pearson Correlation are provided for convenience at recommends.similarities.sim_distance and recommends.similarities.sim_pearson.

  Defaults to recommends.similarities.sim_distance

RecSysAlgorithm

This class implement a SVD algorithm. Requires python-recsys (available at https://github.com/ocelma/python-recsys).

python-recsys in turn requires SciPy, NumPy, and other python libraries.