MyMediaLite  3.01
Public Member Functions | Protected Attributes | Properties
LatentFeatureLogLinearModel Class Reference

Latent-feature log linear model. More...

Inheritance diagram for LatentFeatureLogLinearModel:
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.
void Iterate ()
 Run one iteration (= pass over the training data)
 LatentFeatureLogLinearModel ()
 Default constructor.
virtual void LoadModel (string file)
 Get the model parameters from a file.
override float Predict (int user_id, int item_id)
 Predict rating or score for a given user-item combination.
virtual void SaveModel (string file)
 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

float BiasLearnRate [get, set]
 Learn rate factor for the bias terms.
float BiasReg [get, set]
 regularization factor for the bias terms
bool FrequencyRegularization [get, set]
 Regularization based on rating frequency.
double InitMean [get, set]
 Mean of the normal distribution used to initialize the factors.
double InitStdDev [get, set]
 Standard deviation of the normal distribution used to initialize the factors.
float LearnRate [get, set]
 Learn rate.
OptimizationTarget Loss [get, set]
 The optimization target.
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 over the training data.
virtual IRatings Ratings [get, set]
 The rating data.
float RegI [get, set]
 regularization constant for the item factors
float RegU [get, set]
 regularization constant for the user factors

Detailed Description

Latent-feature log linear model.

Literature:

This recommender supports 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_idthe user ID
item_idthe item ID
Returns:
true if a useful prediction can be made, false otherwise

Implements IRecommender.

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

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.

void Iterate ( ) [inline]

Run one iteration (= pass over the training data)

Implements IIterativeModel.

virtual void LoadModel ( string  filename) [inline, virtual, inherited]
override float Predict ( int  user_id,
int  item_id 
) [inline, virtual]

Predict rating or score for a given user-item combination.

Parameters:
user_idthe user ID
item_idthe item ID
Returns:
the predicted score/rating for the given user-item combination

Implements RatingPredictor.

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

float BiasLearnRate [get, set]

Learn rate factor for the bias terms.

float BiasReg [get, set]

regularization factor for the bias terms

bool FrequencyRegularization [get, set]

Regularization based on rating frequency.

Regularization proportional to the inverse of the square root of the number of ratings associated with the user or item. As described in the paper by Menon and Elkan.

double InitMean [get, set]

Mean of the normal distribution used to initialize the factors.

double InitStdDev [get, set]

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

float LearnRate [get, set]

Learn rate.

The optimization target.

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 over the training data.

Implements IIterativeModel.

virtual IRatings Ratings [get, set, inherited]

The rating data.

Implements IRatingPredictor.

Reimplemented in KNN, FactorWiseMatrixFactorization, TimeAwareRatingPredictor, ItemKNN, and UserKNN.

float RegI [get, set]

regularization constant for the item factors

float RegU [get, set]

regularization constant for the user factors


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