MyMediaLite  3.10
Public Member Functions | Protected Attributes | Properties | List of all members
LatentFeatureLogLinearModel Class Reference

Latent-feature log linear model More...

Inheritance diagram for LatentFeatureLogLinearModel:
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
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
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
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
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 
)
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.

void Iterate ( )
inline

Run one iteration (= pass over the training data)

Implements IIterativeModel.

virtual void LoadModel ( string  filename)
inlinevirtualinherited
override float Predict ( int  user_id,
int  item_id 
)
inlinevirtual

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 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.

virtual void SaveModel ( string  filename)
inlinevirtualinherited
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 Recommender.

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: