C# 클래스 MyMediaLite.ItemRecommendation.SoftMarginRankingMF

Matrix factorization model for item prediction optimized for a soft margin (hinge) ranking loss, using stochastic gradient descent (as in BPR-MF).
Literature: Steffen Rendle: Context-Aware Ranking with Factorization Models. Studies in Computational Intelligence. Springer 2011. http://www.springer.com/engineering/computational+intelligence+and+complexity/book/978-3-642-16897-0 Markus Weimer, Alexandros Karatzoglou, Alex Smola: Improving Maximum Margin Matrix Factorization. Machine Learning Journal 2008. Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, Lars Schmidt-Thieme: BPR: Bayesian Personalized Ranking from Implicit Feedback. UAI 2009. http://www.ismll.uni-hildesheim.de/pub/pdfs/Rendle_et_al2009-Bayesian_Personalized_Ranking.pdf This recommender supports incremental updates.
상속: BPRMF
파일 보기 프로젝트 열기: zenogantner/MyMediaLite 1 사용 예제들

공개 메소드들

메소드 설명
ComputeObjective ( ) : float

Compute approximate loss
SoftMarginRankingMF ( ) : System
ToString ( ) : string

보호된 메소드들

메소드 설명
UpdateFactors ( int u, int i, int j, bool update_u, bool update_i, bool update_j ) : void

Update latent factors according to the stochastic gradient descent update rule

메소드 상세

ComputeObjective() 공개 메소드

Compute approximate loss
public ComputeObjective ( ) : float
리턴 float

SoftMarginRankingMF() 공개 메소드

public SoftMarginRankingMF ( ) : System
리턴 System

ToString() 공개 메소드

public ToString ( ) : string
리턴 string

UpdateFactors() 보호된 메소드

Update latent factors according to the stochastic gradient descent update rule
protected UpdateFactors ( int u, int i, int j, bool update_u, bool update_i, bool update_j ) : void
u int the user ID
i int the ID of the first item
j int the ID of the second item
update_u bool if true, update the user latent factors
update_i bool if true, update the latent factors of the first item
update_j bool if true, update the latent factors of the second item
리턴 void