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

Linear model optimized for BPR More...

Inheritance diagram for BPRLinear:
ItemRecommender IItemAttributeAwareRecommender IIterativeModel Recommender 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 ()
 Perform one iteration of stochastic gradient ascent 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 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)
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 Member Functions

void SampleItemPair (int u, out int i, out int j)
 Sample a pair of items, given a user
void SampleTriple (out int u, out int i, out int j)
 Sample a triple for BPR learning
int SampleUser ()
 Sample a user that has viewed at least one and not all items
virtual void UpdateFeatures (int u, int i, int j)
 Modified feature update method that exploits attribute sparsity

Properties

virtual IPosOnlyFeedback Feedback [get, set]
 the feedback data to be used for training
double InitMean [get, set]
 mean of the Gaussian distribution used to initialize the features
double InitStdev [get, set]
 standard deviation of the normal distribution used to initialize the features
IBooleanMatrix ItemAttributes [get, set]
float LearnRate [get, set]
 Learning rate alpha
int MaxItemID [get, set]
 Maximum item ID
int MaxUserID [get, set]
 Maximum user ID
int NumItemAttributes [get, set]
uint NumIter [get, set]
 Number of iterations over the training data
float Regularization [get, set]
 Regularization parameter

Detailed Description

Linear model optimized for BPR

Literature:

This recommender does NOT support incremental updates.

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

Perform one iteration of stochastic gradient ascent over the training data

Implements IIterativeModel.

override void LoadModel ( string  filename)
inline

Get the model parameters from a file

Parameters
filenamethe name of the file to read from

Implements IRecommender.

override float Predict ( int  user_id,
int  item_id 
)
inline

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 IRecommender.

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.

void SampleItemPair ( int  u,
out int  i,
out int  j 
)
inlineprotected

Sample a pair of items, given a user

Parameters
uthe user ID
ithe ID of the first item
jthe ID of the second item
void SampleTriple ( out int  u,
out int  i,
out int  j 
)
inlineprotected

Sample a triple for BPR learning

Parameters
uthe user ID
ithe ID of the first item
jthe ID of the second item
int SampleUser ( )
inlineprotected

Sample a user that has viewed at least one and not all items

Returns
the user ID
override void SaveModel ( string  filename)
inline

Save the model parameters to a file

Parameters
filenamethe name of the file to write to

Implements IRecommender.

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 ( )
inline

Learn the model parameters of the recommender from the training data

Implements IRecommender.

virtual void UpdateFeatures ( int  u,
int  i,
int  j 
)
inlineprotectedvirtual

Modified feature update method that exploits attribute sparsity

Property Documentation

virtual IPosOnlyFeedback Feedback
getsetinherited

the feedback data to be used for training

double InitMean
getset

mean of the Gaussian distribution used to initialize the features

double InitStdev
getset

standard deviation of the normal distribution used to initialize the features

float LearnRate
getset

Learning rate alpha

int MaxItemID
getsetinherited

Maximum item ID

int MaxUserID
getsetinherited

Maximum user ID

uint NumIter
getset

Number of iterations over the training data

float Regularization
getset

Regularization parameter


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