MyMediaLite
3.10
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Abstract class for SLIM based item predictors proposed by Ning and Karypis More...
Public Member Functions | |
virtual 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 | |
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) |
virtual void | RemoveFeedback (ICollection< Tuple< int, int >> feedback) |
Remove all feedback events by the given user-item combinations | |
virtual void | RemoveItem (int item_id) |
Remove all feedback by one item | |
virtual void | RemoveUser (int user_id) |
Remove all feedback by one user | |
override void | SaveModel (string file) |
Save the model parameters to a file | |
SLIM () | |
Default constructor | |
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 | |
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) | |
ItemKNN | itemKNN |
The item KNN used in the feature selection step |
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 | NumIter [get, set] |
Number of iterations over the training data | |
bool | UpdateItems [get, set] |
bool | UpdateUsers [get, set] |
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:
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inline |
Default constructor
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inlinevirtualinherited |
Add positive feedback events and perform incremental training
feedback | collection of user id - item id tuples |
Implements IIncrementalItemRecommender.
Reimplemented in UserKNN, ItemKNN, MostPopular, and MF.
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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 BPRSLIM, and LeastSquareSLIM.
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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 BPRSLIM, and LeastSquareSLIM.
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inlinevirtual |
Predict rating or score for a given user-item combination
user_id | the user ID |
item_id | the item ID |
Implements Recommender.
Reimplemented in LeastSquareSLIM.
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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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inlinevirtualinherited |
Remove all feedback events by the given user-item combinations
feedback | collection of user id - item id tuples |
Implements IIncrementalItemRecommender.
Reimplemented in UserKNN, MostPopular, ItemKNN, and MF.
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inlinevirtualinherited |
Remove all feedback by one item
item_id | the item ID |
Implements IIncrementalRecommender.
Reimplemented in BPRMF, BPRSLIM, LeastSquareSLIM, MF, and MostPopular.
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inlinevirtualinherited |
Remove all feedback by one user
user_id | the user ID |
Implements IIncrementalRecommender.
Reimplemented in LeastSquareSLIM, MF, and MostPopular.
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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 BPRSLIM, and LeastSquareSLIM.
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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 BPRSLIM, and LeastSquareSLIM.
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protected |
Item weight matrix (the W matrix in the original paper)
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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 iterations over the training data