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Public Member Functions | Protected Member Functions | Protected Attributes | Properties
MultiCoreBPRMF Class Reference

Matrix factorization for BPR on multiple cores. More...

Inheritance diagram for MultiCoreBPRMF:
BPRMF MF IFoldInItemRecommender IncrementalItemRecommender IIterativeModel IRecommender ItemRecommender IIncrementalItemRecommender Recommender IIncrementalRecommender IRecommender

List of all members.

Public Member Functions

override void AddFeedback (ICollection< Tuple< int, int >> feedback)
 Add positive feedback events and perform incremental training.
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
override float ComputeObjective ()
 Compute the current optimization objective (usually loss plus regularization term) of the model.
override void Iterate ()
 Perform one iteration of stochastic gradient ascent over the training data.
override void LoadModel (string file)
 Get the model parameters from a file.
 MultiCoreBPRMF ()
 default constructor
override float Predict (int user_id, int item_id)
 Predict the weight 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 RemoveFeedback (ICollection< Tuple< int, int >> feedback)
 Remove all feedback events by the given user-item combinations.
override void RemoveItem (int item_id)
 Remove all feedback by one item.
override void RemoveUser (int user_id)
 Remove all feedback by one user.
override void SaveModel (string file)
 Save the model parameters to a file.
IList< Tuple< int, float > > ScoreItems (IList< int > accessed_items, IList< int > candidate_items)
 Score a list of items given a list of items that represent a new user.
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

override void AddItem (int item_id)
override void AddUser (int user_id)
void CheckSampling ()
override void InitModel ()
virtual void RetrainItem (int item_id)
 Retrain the latent factors of a given item.
virtual void RetrainUser (int user_id)
 Retrain the latent factors of a given user.
virtual void SampleItemPair (int u, out int i, out int j)
 Sample a pair of items, given a user.
virtual bool SampleOtherItem (int u, int i, out int j)
 Sample another item, given the first one and the user.
virtual void SampleTriple (out int u, out int i, out int j)
 Sample a triple for BPR learning.
virtual int SampleUser ()
 Sample a user that has viewed at least one and not all items.
virtual void UpdateFactors (int u, int i, int j, bool update_u, bool update_i, bool update_j)
 Update latent factors according to the stochastic gradient descent update rule.

Protected Attributes

bool fast_sampling = false
 Fast, but memory-intensive sampling.
int fast_sampling_memory_limit = 1024
 Fast sampling memory limit, in MiB.
float[] item_bias
 Item bias terms.
Matrix< float > item_factors
 Latent item factor matrix.
float learn_rate = 0.05f
 Learning rate alpha.
int num_factors = 10
 Number of latent factors per user/item.
System.Random random
 Random number generator.
float reg_i = 0.0025f
 Regularization parameter for positive item factors.
float reg_j = 0.00025f
 Regularization parameter for negative item factors.
float reg_u = 0.0025f
 Regularization parameter for user factors.
bool update_j = true
 If set (default), update factors for negative sampled items during learning.
Matrix< float > user_factors
 Latent user factor matrix.
IList< IList< int > > user_neg_items
 support data structure for fast sampling
IList< IList< int > > user_pos_items
 support data structure for fast sampling

Properties

float BiasReg [get, set]
 Regularization parameter for the bias term.
int FastSamplingMemoryLimit [get, set]
 Fast sampling memory limit, in MiB.
virtual IPosOnlyFeedback Feedback [get, set]
 the feedback data to be used for training
double InitMean [get, set]
 Mean of the normal distribution used to initialize the latent factors.
double InitStdDev [get, set]
 Standard deviation of the normal distribution used to initialize the latent factors.
float LearnRate [get, set]
 Learning rate alpha.
int MaxItemID [get, set]
 Maximum item ID.
int MaxThreads [get, set]
 the maximum number of threads to use
int MaxUserID [get, set]
 Maximum user ID.
uint NumFactors [get, set]
 Number of latent factors per user/item.
uint NumIter [get, set]
 Number of iterations over the training data.
float RegI [get, set]
 Regularization parameter for positive item factors.
float RegJ [get, set]
 Regularization parameter for negative item factors.
float RegU [get, set]
 Regularization parameter for user factors.
bool UniformUserSampling [get, set]
 Sample uniformly from users.
bool UpdateItems [get, set]
 true if items shall be updated when doing incremental updates
bool UpdateJ [get, set]
 If set (default), update factors for negative sampled items during learning.
bool UpdateUsers [get, set]
 true if users shall be updated when doing incremental updates
bool WithReplacement [get, set]
 Sample positive observations with (true) or without (false) replacement.

Detailed Description

Matrix factorization for BPR on multiple cores.

Literature:

This recommender supports incremental updates, however they are currently not performed on multiple cores.


Constructor & Destructor Documentation

MultiCoreBPRMF ( ) [inline]

default constructor


Member Function Documentation

override void AddFeedback ( ICollection< Tuple< int, int >>  feedback) [inline, virtual, inherited]

Add positive feedback events and perform incremental training.

Parameters:
feedbackcollection of user id - item id tuples

Reimplemented from IncrementalItemRecommender.

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, SlopeOne, Constant, GlobalAverage, UserAverage, ItemAverage, and Random.

Object Clone ( ) [inline, inherited]

create a shallow copy of the object

override float ComputeObjective ( ) [inline, virtual, inherited]

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

Returns:
the current objective; -1 if not implemented

Implements MF.

Reimplemented in SoftMarginRankingMF.

override void Iterate ( ) [inline, virtual]

Perform one iteration of stochastic gradient ascent over the training data.

One iteration is samples number of positive entries in the training matrix times

Reimplemented from BPRMF.

override void LoadModel ( string  filename) [inline, virtual, inherited]

Get the model parameters from a file.

Parameters:
filenamethe name of the file to read from

Reimplemented from MF.

override float Predict ( int  user_id,
int  item_id 
) [inline, virtual, inherited]

Predict the weight for a given user-item combination.

If the user or the item are not known to the recommender, zero is returned. To avoid this behavior for unknown entities, use CanPredict() to check before.

Parameters:
user_idthe user ID
item_idthe item ID
Returns:
the predicted weight

Reimplemented from MF.

Reimplemented in BPRMF_ItemMapping, and BPRMF_UserMapping.

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.

override void RemoveFeedback ( ICollection< Tuple< int, int >>  feedback) [inline, virtual, inherited]

Remove all feedback events by the given user-item combinations.

Parameters:
feedbackcollection of user id - item id tuples

Reimplemented from IncrementalItemRecommender.

override void RemoveItem ( int  item_id) [inline, virtual, inherited]

Remove all feedback by one item.

Parameters:
item_idthe item ID

Reimplemented from IncrementalItemRecommender.

override void RemoveUser ( int  user_id) [inline, virtual, inherited]

Remove all feedback by one user.

Parameters:
user_idthe user ID

Reimplemented from IncrementalItemRecommender.

virtual void RetrainItem ( int  item_id) [inline, protected, virtual, inherited]

Retrain the latent factors of a given item.

Parameters:
item_idthe item ID
virtual void RetrainUser ( int  user_id) [inline, protected, virtual, inherited]

Retrain the latent factors of a given user.

Parameters:
user_idthe user ID
virtual void SampleItemPair ( int  u,
out int  i,
out int  j 
) [inline, protected, virtual, inherited]

Sample a pair of items, given a user.

Parameters:
uthe user ID
ithe ID of the first item
jthe ID of the second item
virtual bool SampleOtherItem ( int  u,
int  i,
out int  j 
) [inline, protected, virtual, inherited]

Sample another item, given the first one and the user.

Parameters:
uthe user ID
ithe ID of the given item
jthe ID of the other item
Returns:
true if the given item was already seen by user u
virtual void SampleTriple ( out int  u,
out int  i,
out int  j 
) [inline, protected, virtual, inherited]

Sample a triple for BPR learning.

Parameters:
uthe user ID
ithe ID of the first item
jthe ID of the second item

Reimplemented in WeightedBPRMF.

virtual int SampleUser ( ) [inline, protected, virtual, inherited]

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

Returns:
the user ID
override void SaveModel ( string  filename) [inline, virtual, inherited]

Save the model parameters to a file.

Parameters:
filenamethe name of the file to write to

Reimplemented from MF.

IList<Tuple<int, float> > ScoreItems ( IList< int >  accessed_items,
IList< int >  candidate_items 
) [inline, inherited]

Score a list of items given a list of items that represent a new user.

Returns:
a list of int and float pairs, representing item IDs and predicted scores
Parameters:
accessed_itemsthe ratings (item IDs and rating values) representing the new user
candidate_itemsthe items to be rated

Implements IFoldInItemRecommender.

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 BPRMF.

virtual void UpdateFactors ( int  u,
int  i,
int  j,
bool  update_u,
bool  update_i,
bool  update_j 
) [inline, protected, virtual, inherited]

Update latent factors according to the stochastic gradient descent update rule.

Parameters:
uthe user ID
ithe ID of the first item
jthe ID of the second item
update_uif true, update the user latent factors
update_iif true, update the latent factors of the first item
update_jif true, update the latent factors of the second item

Reimplemented in SoftMarginRankingMF.


Member Data Documentation

bool fast_sampling = false [protected, inherited]

Fast, but memory-intensive sampling.

int fast_sampling_memory_limit = 1024 [protected, inherited]

Fast sampling memory limit, in MiB.

float [] item_bias [protected, inherited]

Item bias terms.

Matrix<float> item_factors [protected, inherited]

Latent item factor matrix.

float learn_rate = 0.05f [protected, inherited]

Learning rate alpha.

int num_factors = 10 [protected, inherited]

Number of latent factors per user/item.

System.Random random [protected, inherited]

Random number generator.

float reg_i = 0.0025f [protected, inherited]

Regularization parameter for positive item factors.

float reg_j = 0.00025f [protected, inherited]

Regularization parameter for negative item factors.

float reg_u = 0.0025f [protected, inherited]

Regularization parameter for user factors.

bool update_j = true [protected, inherited]

If set (default), update factors for negative sampled items during learning.

Matrix<float> user_factors [protected, inherited]

Latent user factor matrix.

IList<IList<int> > user_neg_items [protected, inherited]

support data structure for fast sampling

IList<IList<int> > user_pos_items [protected, inherited]

support data structure for fast sampling


Property Documentation

float BiasReg [get, set, inherited]

Regularization parameter for the bias term.

int FastSamplingMemoryLimit [get, set, inherited]

Fast sampling memory limit, in MiB.

virtual IPosOnlyFeedback Feedback [get, set, inherited]

the feedback data to be used for training

double InitMean [get, set, inherited]

Mean of the normal distribution used to initialize the latent factors.

double InitStdDev [get, set, inherited]

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

float LearnRate [get, set, inherited]

Learning rate alpha.

int MaxItemID [get, set, inherited]

Maximum item ID.

int MaxThreads [get, set]

the maximum number of threads to use

Determines the number of sections the users and items will be divided into.

int MaxUserID [get, set, inherited]

Maximum user ID.

uint NumFactors [get, set, inherited]

Number of latent factors per user/item.

uint NumIter [get, set, inherited]

Number of iterations over the training data.

Implements IIterativeModel.

float RegI [get, set, inherited]

Regularization parameter for positive item factors.

float RegJ [get, set, inherited]

Regularization parameter for negative item factors.

float RegU [get, set, inherited]

Regularization parameter for user factors.

bool UniformUserSampling [get, set, inherited]

Sample uniformly from users.

bool UpdateItems [get, set, inherited]

true if items shall be updated when doing incremental updates

Set to false if you do not want any updates to the item model parameters when doing incremental updates.

Implements IIncrementalRecommender.

bool UpdateJ [get, set, inherited]

If set (default), update factors for negative sampled items during learning.

bool UpdateUsers [get, set, inherited]

true if users shall be updated when doing incremental updates

Default should be true. Set to false if you do not want any updates to the user model parameters when doing incremental updates.

Implements IIncrementalRecommender.

bool WithReplacement [get, set, inherited]

Sample positive observations with (true) or without (false) replacement.


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