basic.rst

Basic and Utility Algorithms

The :pylenskit.algorithms.basic module contains baseline and utility algorithms for nonpersonalized recommendation and testing.

Most Popular Item Recommendation

The :pyPopScore algorithm scores items by their populariy for enabling most-popular-item recommendation.

lenskit.algorithms.basic

PopScore

Popular

Random Item Recommendation

The :pyRandom algorithm implements random-item recommendation.

lenskit.algorithms.basic

Random

Unrated Item Candidate Selector

:pyUnratedItemCandidateSelector is a candidate selector that remembers items users have rated, and returns a candidate set consisting of all unrated items. It is the default candidate selector for :pyTopN.

lenskit.algorithms.basic

UnratedItemCandidateSelector

Fallback Predictor

The Fallback rating predictor is a simple hybrid that takes a list of composite algorithms, and uses the first one to return a result to predict the rating for each item.

A common case is to fill in with :pyBias when a primary predictor cannot score an item.

lenskit.algorithms.basic

Fallback

Memorized Predictor

The Memorized recommender is primarily useful for test cases. It memorizes a set of rating predictions and returns them.

lenskit.algorithms.basic

Memorized