FactorWiseMatrixFactorization Class Reference

Matrix factorization with factor-wise learning. More...

Inheritance diagram for FactorWiseMatrixFactorization:
RatingPredictor IIterativeModel IRatingPredictor IRecommender

List of all members.

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.
Object Clone ()
 create a shallow copy of the object
float ComputeObjective ()
 Compute the current optimization objective (usually loss plus regularization term) of the model.
 FactorWiseMatrixFactorization ()
 Default constructor.
virtual void Iterate ()
 Run one iteration (= pass over the training data).
override void LoadModel (string filename)
 Get the model parameters from a file.
override float Predict (int user_id, int item_id)
 Predict the rating of a given user for a given item.
override void SaveModel (string filename)
 Save the model parameters to a file.
override string ToString ()
 Return a string representation of the recommender.
override void Train ()
 Learn the model parameters of the recommender from the training data.

Protected Attributes

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

Properties

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

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 
) [inline, virtual, inherited]

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_id the user ID
item_id the item ID
Returns:
true if a useful prediction can be made, false otherwise

Implements IRecommender.

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

Object Clone (  )  [inline, inherited]

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 (  )  [inline, virtual]

Run one iteration (= pass over the training data).

Implements IIterativeModel.

override void LoadModel ( string  filename  )  [inline, virtual]

Get the model parameters from a file.

Parameters:
filename the name of the file to read from

Reimplemented from RatingPredictor.

override float Predict ( int  user_id,
int  item_id 
) [inline, virtual]

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_id the user ID
item_id the item ID
Returns:
the predicted rating

Implements RatingPredictor.

override void SaveModel ( string  filename  )  [inline, virtual]

Save the model parameters to a file.

Parameters:
filename the name of the file to write to

Reimplemented from RatingPredictor.

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.

Reimplemented from RatingPredictor.


Member Data Documentation

float max_rating [protected, inherited]

Maximum rating value.

float min_rating [protected, inherited]

Minimum rating value.

IRatings ratings [protected, inherited]

rating data


Property Documentation

double InitMean [get, set]

Mean of the normal distribution used to initialize the factors.

double InitStdev [get, set]

Standard deviation of the normal distribution used to initialize the factors.

int MaxItemID [get, set, inherited]

Maximum item ID.

virtual float MaxRating [get, set, inherited]

Maximum rating value.

Implements IRatingPredictor.

int MaxUserID [get, set, inherited]

Maximum user ID.

virtual float MinRating [get, set, inherited]

Minimum rating value.

Implements IRatingPredictor.

uint NumFactors [get, set]

Number of latent factors.

uint NumIter [get, set]

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

Implements IIterativeModel.

override IRatings Ratings [set]

The rating data.

Reimplemented from RatingPredictor.

float RegI [get, set]

regularization constant for the item bias of the underlying baseline predictor

float RegU [get, set]

regularization constant for the user bias of the underlying baseline predictor

virtual double Sensibility [get, set]

Sensibility parameter (stopping criterion for parameter fitting).

epsilon in the Bell et al. paper

virtual double Shrinkage [get, set]

Shrinkage parameter.

alpha in the Bell et al. paper


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