Algorithm Summary

LKPY provides general algorithmic concepts, along with implementations of several algorithms. These algorithm interfaces are based on the SciKit design patterns [BLB+13], adapted for Pandas-based data structures.

All algorithms implement the standard interfaces.

Basic Algorithms

bias.Bias([items, users, damping])	A user-item bias rating prediction algorithm.
basic.PopScore([score_method])	Score items by their popularity.
basic.TopN(predictor[, selector])	Basic recommender that implements top-N recommendation using a predictor.
basic.Fallback()	The Fallback algorithm predicts with its first component, uses the second to fill in missing values, and so forth.
basic.UnratedItemCandidateSelector()	CandidateSelector that selects items a user has not rated as candidates.
basic.Memorized(scores)	The memorized algorithm memorizes socres provided at construction time (not training time).

k-NN Algorithms

knn.UserUser(nnbrs[, min_nbrs, min_sim, ...])	User-user nearest-neighbor collaborative filtering with ratings.
knn.ItemItem(nnbrs[, min_nbrs, min_sim, ...])	Item-item nearest-neighbor collaborative filtering with ratings.

Matrix Factorization

als.BiasedMF(features, *[, epochs, reg, ...])	Biased matrix factorization trained with alternating least squares [ZWSP08].
als.ImplicitMF(features, *[, epochs, reg, ...])	Implicit matrix factorization trained with alternating least squares [HKV08].

Add-On Packages

See add-on algorithms for additional algorithm families and bridges to other packages.