MyMediaLite
3.07
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Abstract class for matrix factorization based item predictors. 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. | |
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) |
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. | |
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_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] |
true if items shall be updated when doing incremental updates | |
bool | UpdateUsers [get, set] |
true if users shall be updated when doing incremental updates |
Abstract class for matrix factorization based item predictors.
MF | ( | ) | [inline] |
Default constructor.
virtual void AddFeedback | ( | ICollection< Tuple< int, int >> | feedback | ) | [inline, virtual, inherited] |
Add positive feedback events and perform incremental training.
feedback | collection of user id - item id tuples |
Implements IIncrementalItemRecommender.
Reimplemented in BPRMF, UserKNN, ItemKNN, and MostPopular.
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.
user_id | the user ID |
item_id | the item ID |
Implements IRecommender.
Reimplemented in ExternalItemRecommender, ExternalRatingPredictor, BiPolarSlopeOne, SlopeOne, Constant, GlobalAverage, UserAverage, ItemAverage, and Random.
Object Clone | ( | ) | [inline, inherited] |
create a shallow copy of the object
abstract float ComputeObjective | ( | ) | [pure virtual] |
Compute the current optimization objective (usually loss plus regularization term) of the model.
Implements IIterativeModel.
Implemented in BPRMF, WRMF, CLiMF, and SoftMarginRankingMF.
abstract void Iterate | ( | ) | [pure virtual] |
Iterate once over the data.
Implements IIterativeModel.
Implemented in BPRMF, BPRMF_Mapping, WRMF, and CLiMF.
override void LoadModel | ( | string | filename | ) | [inline, virtual] |
Get the model parameters from a file.
filename | the name of the file to read from |
Reimplemented from Recommender.
Reimplemented in BPRMF.
override float Predict | ( | int | user_id, |
int | item_id | ||
) | [inline, virtual] |
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, BPRMF_ItemMapping, and BPRMF_UserMapping.
IList<Tuple<int, float> > Recommend | ( | int | user_id, |
int | n = -1 , |
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ICollection< int > | ignore_items = null , |
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ICollection< int > | candidate_items = null |
||
) | [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.
virtual void RemoveFeedback | ( | ICollection< Tuple< int, int >> | feedback | ) | [inline, virtual, inherited] |
Remove all feedback events by the given user-item combinations.
feedback | collection of user id - item id tuples |
Implements IIncrementalItemRecommender.
Reimplemented in BPRMF, UserKNN, MostPopular, and ItemKNN.
virtual void RemoveItem | ( | int | item_id | ) | [inline, virtual, inherited] |
Remove all feedback by one item.
item_id | the item ID |
Implements IIncrementalRecommender.
Reimplemented in BPRMF, BPRSLIM, LeastSquareSLIM, and MostPopular.
virtual void RemoveUser | ( | int | user_id | ) | [inline, virtual, inherited] |
Remove all feedback by one user.
user_id | the user ID |
Implements IIncrementalRecommender.
Reimplemented in BPRMF, BPRSLIM, LeastSquareSLIM, and MostPopular.
override void SaveModel | ( | string | filename | ) | [inline, virtual] |
Save the model parameters to a file.
filename | the name of the file to write to |
Reimplemented from Recommender.
Reimplemented in BPRMF.
override string ToString | ( | ) | [inline, inherited] |
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, BPRSLIM, BPRMF_Mapping, SVDPlusPlus, MatrixFactorization, SigmoidCombinedAsymmetricFactorModel, CoClustering, SigmoidItemAsymmetricFactorModel, LeastSquareSLIM, TimeAwareBaseline, SigmoidUserAsymmetricFactorModel, LatentFeatureLogLinearModel, FactorWiseMatrixFactorization, UserItemBaseline, BPRLinear, SigmoidSVDPlusPlus, SocialMF, BPRMF_ItemMapping, BPRMF_UserMapping, KNN, NaiveBayes, KNN, MostPopular, TimeAwareBaselineWithFrequencies, WRMF, BPRMF_ItemMapping_Optimal, CLiMF, SoftMarginRankingMF, BPRMF_ItemMappingSVR, ItemAttributeSVM, BPRMF_UserMapping_Optimal, BPRMF_ItemMappingKNN, ExternalItemRecommender, ExternalRatingPredictor, WeightedBPRMF, MultiCoreBPRMF, and Constant.
Matrix<float> item_factors [protected] |
Latent item factor matrix.
int num_factors = 10 [protected] |
Number of latent factors per user/item.
Matrix<float> user_factors [protected] |
Latent user factor matrix.
virtual IPosOnlyFeedback Feedback [get, set, inherited] |
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, inherited] |
Maximum item ID.
int MaxUserID [get, set, inherited] |
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.
Implements IIterativeModel.
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 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.