MyMediaLite
3.10
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Abstract class for matrix factorization based item predictors More...
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 | |
abstract float | ComputeObjective () |
Compute the current optimization objective (usually loss plus regularization term) of the model | |
abstract void | Iterate () |
Iterate once over the data | |
override void | LoadModel (string file) |
Get the model parameters from a file | |
MF () | |
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 | |
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) |
virtual void | InitModel () |
abstract void | RetrainItem (int item_id) |
Retrain the latent factors of a given item | |
abstract void | RetrainUser (int user_id) |
Retrain the latent factors of a given user |
Protected Attributes | |
Matrix< float > | item_factors |
Latent item factor matrix | |
int | num_factors = 10 |
Number of latent factors per user/item | |
Matrix< float > | user_factors |
Latent user factor matrix |
Properties | |
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 | |
int | MaxItemID [get, set] |
Maximum item ID | |
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 | |
bool | UpdateItems [get, set] |
bool | UpdateUsers [get, set] |
Abstract class for matrix factorization based item predictors
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inline |
Default constructor
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inlinevirtual |
Add positive feedback events and perform incremental training
feedback | collection of user id - item id tuples |
Reimplemented from IncrementalItemRecommender.
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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.
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inlineinherited |
create a shallow copy of the object
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pure virtual |
Compute the current optimization objective (usually loss plus regularization term) of the model
Implements IIterativeModel.
Implemented in BPRMF, WRMF, and SoftMarginRankingMF.
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pure virtual |
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inlinevirtual |
Get the model parameters from a file
filename | the name of the file to read from |
Reimplemented from Recommender.
Reimplemented in BPRMF.
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inlinevirtual |
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.
user_id | the user ID |
item_id | the item ID |
Implements Recommender.
Reimplemented in BPRMF.
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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.
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inlinevirtual |
Remove all feedback events by the given user-item combinations
feedback | collection of user id - item id tuples |
Reimplemented from IncrementalItemRecommender.
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inlinevirtual |
Remove all feedback by one item
item_id | the item ID |
Reimplemented from IncrementalItemRecommender.
Reimplemented in BPRMF.
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inlinevirtual |
Remove all feedback by one user
user_id | the user ID |
Reimplemented from IncrementalItemRecommender.
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protectedpure virtual |
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protectedpure virtual |
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inlinevirtual |
Save the model parameters to a file
filename | the name of the file to write to |
Reimplemented from Recommender.
Reimplemented in BPRMF.
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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.
Reimplemented in BPRMF, BiasedMatrixFactorization, SVDPlusPlus, MatrixFactorization, SigmoidCombinedAsymmetricFactorModel, CoClustering, BPRSLIM, SigmoidItemAsymmetricFactorModel, LeastSquareSLIM, TimeAwareBaseline, SigmoidUserAsymmetricFactorModel, LatentFeatureLogLinearModel, FactorWiseMatrixFactorization, UserItemBaseline, SigmoidSVDPlusPlus, SocialMF, BPRLinear, KNN, NaiveBayes, WRMF, KNN, MostPopular, TimeAwareBaselineWithFrequencies, SoftMarginRankingMF, ExternalItemRecommender, ExternalRatingPredictor, WeightedBPRMF, MultiCoreBPRMF, and Constant.
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inlinevirtual |
Learn the model parameters of the recommender from the training data
Implements Recommender.
Reimplemented in BPRMF, MultiCoreBPRMF, and WeightedBPRMF.
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protected |
Latent item factor matrix
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protected |
Number of latent factors per user/item
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protected |
Latent user factor matrix
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getsetinherited |
the feedback data to be used for training
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getset |
Mean of the normal distribution used to initialize the latent factors
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getset |
Standard deviation of the normal distribution used to initialize the latent factors
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getsetinherited |
Maximum item ID
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getsetinherited |
Maximum user ID
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getset |
Number of latent factors per user/item
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getset |
Number of iterations over the training data