MyMediaLite  3.11
Public Member Functions | Protected Member Functions | Protected Attributes | Properties | List of all members
SLIM Class Referenceabstract

Abstract class for SLIM based item predictors proposed by Ning and Karypis More...

Inheritance diagram for SLIM:
IncrementalItemRecommender IIterativeModel ItemRecommender IIncrementalItemRecommender Recommender IIncrementalRecommender IRecommender BPRSLIM LeastSquareSLIM

Public Member Functions

virtual void AddFeedback (ICollection< Tuple< int, int >> feedback)
 Add positive feedback events and perform incremental training More...
 
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...
 
abstract float ComputeObjective ()
 Compute the current optimization objective (usually loss plus regularization term) of the model More...
 
abstract void Iterate ()
 Iterate once over the data More...
 
override 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 RemoveFeedback (ICollection< Tuple< int, int >> feedback)
 Remove all feedback events by the given user-item combinations More...
 
virtual void RemoveItem (int item_id)
 Remove all feedback by one item More...
 
virtual void RemoveUser (int user_id)
 Remove all feedback by one user More...
 
override void SaveModel (string file)
 Save the model parameters to a file More...
 
 SLIM ()
 Default constructor 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 Member Functions

virtual void AddItem (int item_id)
 
virtual void AddUser (int user_id)
 
virtual void InitModel ()
 

Protected Attributes

Matrix< float > item_weights
 Item weight matrix (the W matrix in the original paper) More...
 
ItemKNN itemKNN
 The item KNN used in the feature selection step More...
 

Properties

virtual IPosOnlyFeedback Feedback [get, set]
 the feedback data to be used for training More...
 
double InitMean [get, set]
 Mean of the normal distribution used to initialize the latent factors More...
 
double InitStdDev [get, set]
 Standard deviation of the normal distribution used to initialize the latent factors More...
 
int MaxItemID [get, set]
 Maximum item ID More...
 
int MaxUserID [get, set]
 Maximum user ID More...
 
uint NumIter [get, set]
 Number of iterations over the training data More...
 
bool UpdateItems [get, set]
 
bool UpdateUsers [get, set]
 

Detailed Description

Abstract class for SLIM based item predictors proposed by Ning and Karypis

This class only implements the prediction model presented in the original paper.

Literature:

Constructor & Destructor Documentation

SLIM ( )
inline

Default constructor

Member Function Documentation

virtual void AddFeedback ( ICollection< Tuple< int, int >>  feedback)
inlinevirtualinherited

Add positive feedback events and perform incremental training

Parameters
feedbackcollection of user id - item id tuples

Implements IIncrementalItemRecommender.

Reimplemented in UserKNN, ItemKNN, MostPopular, and MF.

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

abstract float ComputeObjective ( )
pure virtual

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

Returns
the current objective; -1 if not implemented

Implements IIterativeModel.

Implemented in BPRSLIM, and LeastSquareSLIM.

abstract void Iterate ( )
pure virtual

Iterate once over the data

Implements IIterativeModel.

Implemented in LeastSquareSLIM, and BPRSLIM.

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 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 RemoveFeedback ( ICollection< Tuple< int, int >>  feedback)
inlinevirtualinherited

Remove all feedback events by the given user-item combinations

Parameters
feedbackcollection of user id - item id tuples

Implements IIncrementalItemRecommender.

Reimplemented in UserKNN, MostPopular, ItemKNN, and MF.

virtual void RemoveItem ( int  item_id)
inlinevirtualinherited

Remove all feedback by one item

Parameters
item_idthe item ID

Implements IIncrementalRecommender.

Reimplemented in BPRMF, BPRSLIM, LeastSquareSLIM, MF, and MostPopular.

virtual void RemoveUser ( int  user_id)
inlinevirtualinherited

Remove all feedback by one user

Parameters
user_idthe user ID

Implements IIncrementalRecommender.

Reimplemented in LeastSquareSLIM, MF, and MostPopular.

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 ( )
inlineinherited

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

Matrix<float> item_weights
protected

Item weight matrix (the W matrix in the original paper)

ItemKNN itemKNN
protected

The item KNN used in the feature selection step

Property Documentation

virtual IPosOnlyFeedback Feedback
getsetinherited

the feedback data to be used for training

double InitMean
getset

Mean of the normal distribution used to initialize the latent factors

double InitStdDev
getset

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

int MaxItemID
getsetinherited

Maximum item ID

int MaxUserID
getsetinherited

Maximum user ID

uint NumIter
getset

Number of iterations over the training data


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