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FactorWiseMatrixFactorization Class Reference

Matrix factorization with factor-wise learning More...

Inheritance diagram for FactorWiseMatrixFactorization:
RatingPredictor IIterativeModel Recommender IRatingPredictor IRecommender IRecommender

Public Member Functions

virtual bool CanPredict (int user_id, int item_id)
 Check whether a useful prediction (i.e. not using a fallback/default answer) can be made for a given user-item combination More...
 
Object Clone ()
 create a shallow copy of the object More...
 
float ComputeObjective ()
 Compute the current optimization objective (usually loss plus regularization term) of the model More...
 
 FactorWiseMatrixFactorization ()
 Default constructor More...
 
virtual void Iterate ()
 Run one iteration (= pass over the training data) More...
 
override void LoadModel (string filename)
 Get the model parameters from a file More...
 
override float Predict (int user_id, int item_id)
 Predict the rating of a given user for a given item More...
 
IList< Tuple< int, float > > Recommend (int user_id, int n=-1, ICollection< int > ignore_items=null, ICollection< int > candidate_items=null)
 Recommend items for a given user More...
 
virtual System.Collections.Generic.IList< Tuple< int, float > > Recommend (int user_id, int n=-1, System.Collections.Generic.ICollection< int > ignore_items=null, System.Collections.Generic.ICollection< int > candidate_items=null)
 
override void SaveModel (string filename)
 Save the model parameters to a file More...
 
override string ToString ()
 Return a string representation of the recommender More...
 
override void Train ()
 Learn the model parameters of the recommender from the training data More...
 

Protected Attributes

float max_rating
 Maximum rating value More...
 
float min_rating
 Minimum rating value More...
 
IRatings ratings
 rating data More...
 

Properties

double InitMean [get, set]
 Mean of the normal distribution used to initialize the factors More...
 
double InitStdev [get, set]
 Standard deviation of the normal distribution used to initialize the factors More...
 
int MaxItemID [get, set]
 Maximum item ID More...
 
virtual float MaxRating [get, set]
 Maximum rating value More...
 
int MaxUserID [get, set]
 Maximum user ID More...
 
virtual float MinRating [get, set]
 Minimum rating value More...
 
uint NumFactors [get, set]
 Number of latent factors More...
 
uint NumIter [get, set]
 Number of iterations (in this case: number of latent factors) More...
 
override IRatings Ratings [set]
 
float RegI [get, set]
 regularization constant for the item bias of the underlying baseline predictor More...
 
float RegU [get, set]
 regularization constant for the user bias of the underlying baseline predictor More...
 
virtual double Sensibility [get, set]
 Sensibility parameter (stopping criterion for parameter fitting) More...
 
virtual double Shrinkage [get, set]
 Shrinkage parameter More...
 

Detailed Description

Matrix factorization with factor-wise learning

Similar to the approach described in Simon Funk's seminal blog post: http://sifter.org/~simon/journal/20061211.html

Literature:

This recommender does NOT support incremental updates.

Constructor & Destructor Documentation

Default constructor

Member Function Documentation

virtual bool CanPredict ( int  user_id,
int  item_id 
)
inlinevirtualinherited

Check whether a useful prediction (i.e. not using a fallback/default answer) can be made for a given user-item combination

It is up to the recommender implementor to decide when a prediction is useful, and to document it accordingly.

Parameters
user_idthe user ID
item_idthe item ID
Returns
true if a useful prediction can be made, false otherwise

Implements IRecommender.

Reimplemented in ExternalItemRecommender, ExternalRatingPredictor, BiPolarSlopeOne, SlopeOne, Constant, GlobalAverage, UserAverage, ItemAverage, and Random.

Object Clone ( )
inlineinherited

create a shallow copy of the object

float ComputeObjective ( )
inline

Compute the current optimization objective (usually loss plus regularization term) of the model

Returns
the current objective; -1 if not implemented

Implements IIterativeModel.

virtual void Iterate ( )
inlinevirtual

Run one iteration (= pass over the training data)

Implements IIterativeModel.

override void LoadModel ( string  filename)
inlinevirtual

Get the model parameters from a file

Parameters
filenamethe name of the file to read from

Reimplemented from Recommender.

override float Predict ( int  user_id,
int  item_id 
)
inlinevirtual

Predict the rating of a given user for a given item

If the user or the item are not known to the recommender, the global effects prediction is returned. To avoid this behavior for unknown entities, use CanPredict() to check before.

Parameters
user_idthe user ID
item_idthe item ID
Returns
the predicted rating

Implements Recommender.

IList<Tuple<int, float> > Recommend ( int  user_id,
int  n = -1,
ICollection< int >  ignore_items = null,
ICollection< int >  candidate_items = null 
)
inherited

Recommend items for a given user

Parameters
user_idthe user ID
nthe number of items to recommend, -1 for as many as possible
ignore_itemscollection if items that should not be returned; if null, use empty collection
candidate_itemsthe candidate items to choose from; if null, use all items
Returns
a sorted list of (item_id, score) tuples

Implemented in WeightedEnsemble, and Ensemble.

override void SaveModel ( string  filename)
inlinevirtual

Save the model parameters to a file

Parameters
filenamethe name of the file to write to

Reimplemented from Recommender.

override string ToString ( )
inline

Return a string representation of the recommender

The ToString() method of recommenders should list the class name and all hyperparameters, separated by space characters.

Implements IRecommender.

override void Train ( )
inlinevirtual

Learn the model parameters of the recommender from the training data

Implements Recommender.

Member Data Documentation

float max_rating
protectedinherited

Maximum rating value

float min_rating
protectedinherited

Minimum rating value

IRatings ratings
protectedinherited

rating data

Property Documentation

double InitMean
getset

Mean of the normal distribution used to initialize the factors

double InitStdev
getset

Standard deviation of the normal distribution used to initialize the factors

int MaxItemID
getsetinherited

Maximum item ID

virtual float MaxRating
getsetinherited

Maximum rating value

int MaxUserID
getsetinherited

Maximum user ID

virtual float MinRating
getsetinherited

Minimum rating value

uint NumFactors
getset

Number of latent factors

uint NumIter
getset

Number of iterations (in this case: number of latent factors)

float RegI
getset

regularization constant for the item bias of the underlying baseline predictor

float RegU
getset

regularization constant for the user bias of the underlying baseline predictor

virtual double Sensibility
getset

Sensibility parameter (stopping criterion for parameter fitting)

epsilon in the Bell et al. paper

virtual double Shrinkage
getset

Shrinkage parameter

alpha in the Bell et al. paper


The documentation for this class was generated from the following file: