Latent-feature log linear model More...

Inheritance diagram for LatentFeatureLogLinearModel:

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...

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

	LatentFeatureLogLinearModel ()
	Default constructor More...

virtual void	LoadModel (string file)
	Get the model parameters from a file More...

override float	Predict (int user_id, int item_id)
	Predict rating or score for a given user-item combination 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)

virtual void	SaveModel (string file)
	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
float	BiasLearnRate `[get, set]`
	Learn rate factor for the bias terms More...

float	BiasReg `[get, set]`
	regularization factor for the bias terms More...

bool	FrequencyRegularization `[get, set]`
	Regularization based on rating frequency More...

double	InitMean `[get, set]`
	Mean of the normal distribution used to initialize the factors More...

double	InitStdDev `[get, set]`
	Standard deviation of the normal distribution used to initialize the factors More...

float	LearnRate `[get, set]`
	Learn rate More...

OptimizationTarget	Loss `[get, set]`
	The optimization target 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 over the training data More...

virtual IRatings	Ratings `[get, set]`
	The rating data More...

float	RegI `[get, set]`
	regularization constant for the item factors More...

float	RegU `[get, set]`
	regularization constant for the user factors More...

Detailed Description

Latent-feature log linear model

Literature:

Aditya Krishna Menon, Charles Elkan: A log-linear model with latent features for dyadic prediction. ICDM 2010. http://cseweb.ucsd.edu/~akmenon/LFL-ICDM10.pdf

This recommender supports incremental updates.

Constructor & Destructor Documentation

LatentFeatureLogLinearModel ( )

inline

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_id	the user ID
item_id	the 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.

void Iterate ( )

inline

Run one iteration (= pass over the training data)

Implements IIterativeModel.

virtual void LoadModel ( string filename )

inlinevirtualinherited

Get the model parameters from a file

Parameters

filename the name of the file to read from

Implements IRecommender.

Reimplemented in BPRMF, MatrixFactorization, BiasedMatrixFactorization, BPRSLIM, CoClustering, LeastSquareSLIM, SVDPlusPlus, UserItemBaseline, FactorWiseMatrixFactorization, SigmoidCombinedAsymmetricFactorModel, MF, SigmoidSVDPlusPlus, BiPolarSlopeOne, SigmoidItemAsymmetricFactorModel, SigmoidUserAsymmetricFactorModel, KNN, KNN, MostPopular, NaiveBayes, SlopeOne, SLIM, MostPopularByAttributes, EntityAverage, GlobalAverage, ExternalItemRecommender, ExternalRatingPredictor, Constant, Random, Random, and Zero.

override float Predict	(	int	user_id,
		int	item_id
	)

inlinevirtual

Predict rating or score for a given user-item combination

Parameters

user_id	the user ID
item_id	the item ID

Returns: the predicted score/rating for the given user-item combination

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_id	the user ID
n	the number of items to recommend, -1 for as many as possible
ignore_items	collection if items that should not be returned; if null, use empty collection
candidate_items	the candidate items to choose from; if null, use all items

Returns: a sorted list of (item_id, score) tuples

Implemented in WeightedEnsemble, and Ensemble.

virtual void SaveModel ( string filename )

inlinevirtualinherited

Save the model parameters to a file

Parameters

filename the name of the file to write to

Implements IRecommender.

Reimplemented in BPRMF, MatrixFactorization, BiasedMatrixFactorization, BPRSLIM, CoClustering, LeastSquareSLIM, SVDPlusPlus, UserItemBaseline, FactorWiseMatrixFactorization, BiPolarSlopeOne, SigmoidCombinedAsymmetricFactorModel, MF, NaiveBayes, SigmoidItemAsymmetricFactorModel, SigmoidUserAsymmetricFactorModel, SlopeOne, KNN, MostPopular, KNN, SLIM, MostPopularByAttributes, EntityAverage, ExternalItemRecommender, ExternalRatingPredictor, GlobalAverage, Constant, Random, Random, and Zero.

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

float BiasLearnRate

getset

Learn rate factor for the bias terms

float BiasReg

getset

regularization factor for the bias terms

bool FrequencyRegularization

getset

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

getset

Mean of the normal distribution used to initialize the factors

double InitStdDev

getset

Standard deviation of the normal distribution used to initialize the factors

float LearnRate

getset

Learn rate

OptimizationTarget Loss

getset

The optimization target

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 over the training data

virtual IRatings Ratings

getsetinherited

The rating data

float RegI

getset

regularization constant for the item factors

float RegU

getset

regularization constant for the user factors

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

LatentFeatureLogLinearModel.cs

Public Member Functions

Protected Attributes

Properties

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation

Property Documentation