MyMediaLite
3.11
|
Matrix factorization for BPR on multiple cores More...
Public Member Functions | |
override 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... | |
override float | ComputeObjective () |
Compute the current optimization objective (usually loss plus regularization term) of the model More... | |
override void | Iterate () |
Perform one iteration of stochastic gradient ascent over the training data More... | |
override void | LoadModel (string file) |
Get the model parameters from a file More... | |
MultiCoreBPRMF () | |
default constructor 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) |
override void | RemoveFeedback (ICollection< Tuple< int, int >> feedback) |
Remove all feedback events by the given user-item combinations More... | |
override void | RemoveItem (int item_id) |
Remove all feedback by one item More... | |
override 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... | |
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 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 | |
override void | AddItem (int item_id) |
override void | AddUser (int user_id) |
override void | InitModel () |
override void | IterateWithoutReplacementUniformPair () |
Iterate over the training data, uniformly sample from user-item pairs without replacement. More... | |
virtual void | IterateWithoutReplacementUniformPair (IList< int > indices) |
Iterate over the training data, uniformly sample from user-item pairs without replacement. More... | |
virtual void | IterateWithoutReplacementUniformUser () |
Iterate over the training data, uniformly sample from users without replacement. More... | |
virtual void | IterateWithReplacementUniformPair () |
Iterate over the training data, uniformly sample from user-item pairs with replacement. More... | |
virtual void | IterateWithReplacementUniformUser () |
Iterate over the training data, uniformly sample from users with replacement. More... | |
override void | RetrainItem (int item_id) |
Retrain the latent factors of a given item More... | |
override void | RetrainUser (int user_id) |
Retrain the latent factors of a given user More... | |
virtual void | SampleItemPair (ICollection< int > user_items, out int item_id, out int other_item_id) |
Sample a pair of items, given a user More... | |
virtual bool | SampleOtherItem (int user_id, int item_id, out int other_item_id) |
Sample another item, given the first one and the user More... | |
virtual void | SampleTriple (out int user_id, out int item_id, out int other_item_id) |
Sample a triple for BPR learning More... | |
virtual int | SampleUser () |
Uniformly sample a user that has viewed at least one and not all items More... | |
virtual void | UpdateFactors (int user_id, int item_id, int other_item_id, bool update_u, bool update_i, bool update_j) |
Update latent factors according to the stochastic gradient descent update rule More... | |
Protected Attributes | |
float[] | item_bias |
Item bias terms More... | |
Matrix< float > | item_factors |
Latent item factor matrix More... | |
float | learn_rate = 0.05f |
Learning rate alpha More... | |
int | num_factors = 10 |
Number of latent factors per user/item More... | |
float | reg_i = 0.0025f |
Regularization parameter for positive item factors More... | |
float | reg_j = 0.00025f |
Regularization parameter for negative item factors More... | |
float | reg_u = 0.0025f |
Regularization parameter for user factors More... | |
bool | update_j = true |
If set (default), update factors for negative sampled items during learning More... | |
Matrix< float > | user_factors |
Latent user factor matrix More... | |
Static Protected Attributes | |
static System.Random | random |
Reference to (per-thread) singleton random number generator More... | |
Properties | |
float | BiasReg [get, set] |
Regularization parameter for the bias term More... | |
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... | |
float | LearnRate [get, set] |
Learning rate alpha More... | |
int | MaxItemID [get, set] |
Maximum item ID More... | |
int | MaxThreads [get, set] |
the maximum number of threads to use More... | |
int | MaxUserID [get, set] |
Maximum user ID More... | |
uint | NumFactors [get, set] |
Number of latent factors per user/item More... | |
uint | NumIter [get, set] |
Number of iterations over the training data More... | |
float | RegI [get, set] |
Regularization parameter for positive item factors More... | |
float | RegJ [get, set] |
Regularization parameter for negative item factors More... | |
float | RegU [get, set] |
Regularization parameter for user factors More... | |
bool | UniformUserSampling [get, set] |
Sample uniformly from users More... | |
bool | UpdateItems [get, set] |
bool | UpdateJ [get, set] |
If set (default), update factors for negative sampled items during learning More... | |
bool | UpdateUsers [get, set] |
bool | WithReplacement [get, set] |
Sample positive observations with (true) or without (false) replacement More... | |
Matrix factorization for BPR on multiple cores
This recommender supports incremental updates, however they are currently not performed on multiple cores.
|
inline |
default constructor
|
inlinevirtualinherited |
Add positive feedback events and perform incremental training
feedback | collection of user id - item id tuples |
Reimplemented from IncrementalItemRecommender.
|
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.
user_id | the user ID |
item_id | the item ID |
Implements IRecommender.
Reimplemented in ExternalItemRecommender, ExternalRatingPredictor, BiPolarSlopeOne, SlopeOne, Constant, GlobalAverage, UserAverage, ItemAverage, and Random.
|
inlineinherited |
create a shallow copy of the object
|
inlinevirtualinherited |
Compute the current optimization objective (usually loss plus regularization term) of the model
Implements MF.
Reimplemented in SoftMarginRankingMF.
|
inlinevirtualinherited |
Perform one iteration of stochastic gradient ascent over the training data
One iteration is samples number of positive entries in the training matrix times
Implements MF.
|
inlineprotectedvirtual |
Iterate over the training data, uniformly sample from user-item pairs without replacement.
Reimplemented from BPRMF.
|
inlineprotectedvirtualinherited |
Iterate over the training data, uniformly sample from user-item pairs without replacement.
|
inlineprotectedvirtualinherited |
Iterate over the training data, uniformly sample from users without replacement.
|
inlineprotectedvirtualinherited |
Iterate over the training data, uniformly sample from user-item pairs with replacement.
|
inlineprotectedvirtualinherited |
Iterate over the training data, uniformly sample from users with replacement.
|
inlineinherited |
Get the model parameters from a file
filename | the name of the file to read from |
Implements IRecommender.
|
inlineinherited |
Predict rating or score for a given user-item combination
user_id | the user ID |
item_id | the item ID |
Implements IRecommender.
|
inherited |
Recommend items for a given user
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 |
Implemented in WeightedEnsemble, and Ensemble.
|
inlinevirtualinherited |
Remove all feedback events by the given user-item combinations
feedback | collection of user id - item id tuples |
Reimplemented from IncrementalItemRecommender.
|
inlinevirtualinherited |
Remove all feedback by one item
item_id | the item ID |
Reimplemented from IncrementalItemRecommender.
|
inlinevirtualinherited |
Remove all feedback by one user
user_id | the user ID |
Reimplemented from IncrementalItemRecommender.
|
inlineprotectedvirtualinherited |
|
inlineprotectedvirtualinherited |
|
inlineprotectedvirtualinherited |
Sample a pair of items, given a user
user_items | the items accessed by the given user |
item_id | the ID of the first item |
other_item_id | the ID of the second item |
|
inlineprotectedvirtualinherited |
Sample another item, given the first one and the user
user_id | the user ID |
item_id | the ID of the given item |
other_item_id | the ID of the other item |
|
inlineprotectedvirtualinherited |
Sample a triple for BPR learning
user_id | the user ID |
item_id | the ID of the first item |
other_item_id | the ID of the second item |
Reimplemented in WeightedBPRMF.
|
inlineprotectedvirtualinherited |
Uniformly sample a user that has viewed at least one and not all items
|
inlineinherited |
Save the model parameters to a file
filename | the name of the file to write to |
Implements IRecommender.
|
inlineinherited |
Score a list of items given a list of items that represent a new user
accessed_items | the ratings (item IDs and rating values) representing the new user |
candidate_items | the items to be rated |
Implements IFoldInItemRecommender.
|
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.
|
inlinevirtual |
Learn the model parameters of the recommender from the training data
Reimplemented from MF.
|
inlineprotectedvirtualinherited |
Update latent factors according to the stochastic gradient descent update rule
user_id | the user ID |
item_id | the ID of the first item |
other_item_id | the ID of the second item |
update_u | if true, update the user latent factors |
update_i | if true, update the latent factors of the first item |
update_j | if true, update the latent factors of the second item |
Reimplemented in SoftMarginRankingMF.
|
protectedinherited |
Item bias terms
|
protectedinherited |
Latent item factor matrix
|
protectedinherited |
Learning rate alpha
|
protectedinherited |
Number of latent factors per user/item
|
staticprotectedinherited |
Reference to (per-thread) singleton random number generator
|
protectedinherited |
Regularization parameter for positive item factors
|
protectedinherited |
Regularization parameter for negative item factors
|
protectedinherited |
Regularization parameter for user factors
|
protectedinherited |
If set (default), update factors for negative sampled items during learning
|
protectedinherited |
Latent user factor matrix
|
getsetinherited |
Regularization parameter for the bias term
|
getsetinherited |
the feedback data to be used for training
|
getsetinherited |
Mean of the normal distribution used to initialize the latent factors
|
getsetinherited |
Standard deviation of the normal distribution used to initialize the latent factors
|
getsetinherited |
Learning rate alpha
|
getsetinherited |
Maximum item ID
|
getset |
the maximum number of threads to use
Determines the number of sections the users and items will be divided into.
|
getsetinherited |
Maximum user ID
|
getsetinherited |
Number of latent factors per user/item
|
getsetinherited |
Number of iterations over the training data
|
getsetinherited |
Regularization parameter for positive item factors
|
getsetinherited |
Regularization parameter for negative item factors
|
getsetinherited |
Regularization parameter for user factors
|
getsetinherited |
Sample uniformly from users
|
getsetinherited |
If set (default), update factors for negative sampled items during learning
|
getsetinherited |
Sample positive observations with (true) or without (false) replacement