MyMediaLite
3.07
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Interface for rating predictors which support incremental training. More...
Public Member Functions | |
void | AddRatings (IRatings ratings) |
Add new ratings and perform incremental training. | |
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. | |
void | LoadModel (string filename) |
Get the model parameters from a file. | |
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. | |
void | RemoveItem (int item_id) |
Remove all feedback by one item. | |
void | RemoveRatings (IDataSet ratings) |
Remove existing ratings and perform "incremental" training. | |
void | RemoveUser (int user_id) |
Remove all feedback by one user. | |
void | SaveModel (string filename) |
Save the model parameters to a file. | |
string | ToString () |
Return a string representation of the recommender. | |
void | Train () |
Learn the model parameters of the recommender from the training data. | |
void | UpdateRatings (IRatings ratings) |
Update existing ratings and perform incremental training. | |
Properties | |
float | MaxRating [get, set] |
Gets or sets the maximum rating. | |
float | MinRating [get, set] |
Gets or sets the minimum rating. | |
IRatings | Ratings [get, set] |
the ratings to learn from | |
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 |
Interface for rating predictors which support incremental training.
By incremental training we mean that after each update, the recommender does not perform a complete re-training using all data, but only a brief update procedure taking into account the update and only a subset of the existing training data.
This interface does not prevent you from doing a complete re-training when implementing a new class. This makes sense e.g. for simple average-based models.
This interface assumes that every user can rate every item only once.
void AddRatings | ( | IRatings | ratings | ) |
Add new ratings and perform incremental training.
ratings | the ratings |
Implemented in MatrixFactorization, UserItemBaseline, NaiveBayes, ItemKNN, UserKNN, UserAverage, GlobalAverage, ItemAverage, and IncrementalRatingPredictor.
bool CanPredict | ( | int | user_id, |
int | item_id | ||
) | [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 |
Implemented in Ensemble, ExternalItemRecommender, ExternalRatingPredictor, BiPolarSlopeOne, Recommender, SlopeOne, Constant, GlobalAverage, UserAverage, ItemAverage, and Random.
void LoadModel | ( | string | filename | ) | [inherited] |
Get the model parameters from a file.
filename | the name of the file to read from |
Implemented in BPRMF, BPRSLIM, MatrixFactorization, BiasedMatrixFactorization, CoClustering, LeastSquareSLIM, SVDPlusPlus, BPRLinear, UserItemBaseline, FactorWiseMatrixFactorization, SigmoidCombinedAsymmetricFactorModel, SigmoidSVDPlusPlus, BiPolarSlopeOne, SigmoidItemAsymmetricFactorModel, SigmoidUserAsymmetricFactorModel, KNN, KNN, MostPopular, NaiveBayes, SlopeOne, SLIM, ItemAttributeSVM, MostPopularByAttributes, MF, EntityAverage, Recommender, Ensemble, WeightedEnsemble, GlobalAverage, ExternalItemRecommender, ExternalRatingPredictor, Constant, Random, Random, and Zero.
float Predict | ( | int | user_id, |
int | item_id | ||
) | [inherited] |
Predict rating or score for a given user-item combination.
user_id | the user ID |
item_id | the item ID |
Implemented in BPRMF, BiasedMatrixFactorization, LatentFeatureLogLinearModel, LeastSquareSLIM, MatrixFactorization, TimeAwareBaseline, BPRMF_ItemMapping, BPRLinear, BPRMF_UserMapping, FactorWiseMatrixFactorization, GSVDPlusPlus, UserItemBaseline, CoClustering, NaiveBayes, SVDPlusPlus, ItemAttributeSVM, SLIM, SigmoidCombinedAsymmetricFactorModel, MF, MostPopularByAttributes, SigmoidSVDPlusPlus, SigmoidItemAsymmetricFactorModel, SigmoidUserAsymmetricFactorModel, Ensemble, MostPopular, BiPolarSlopeOne, ExternalItemRecommender, ExternalRatingPredictor, ItemKNN, ItemKNN, UserKNN, SlopeOne, WeightedEnsemble, Constant, UserKNN, GlobalAverage, UserAverage, ItemAverage, Recommender, Random, Random, and Zero.
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.
void RemoveItem | ( | int | item_id | ) | [inherited] |
Remove all feedback by one item.
item_id | the item ID |
Implemented in BiasedMatrixFactorization, BPRMF, BPRSLIM, MatrixFactorization, LeastSquareSLIM, IncrementalRatingPredictor, IncrementalItemRecommender, MostPopular, and ItemAverage.
void RemoveRatings | ( | IDataSet | ratings | ) |
Remove existing ratings and perform "incremental" training.
ratings | the user and item IDs of the ratings to be removed |
Implemented in MatrixFactorization, UserItemBaseline, NaiveBayes, ItemKNN, UserKNN, IncrementalRatingPredictor, UserAverage, ItemAverage, and GlobalAverage.
void RemoveUser | ( | int | user_id | ) | [inherited] |
Remove all feedback by one user.
user_id | the user ID |
Implemented in BiasedMatrixFactorization, BPRMF, BPRSLIM, MatrixFactorization, LeastSquareSLIM, MostPopular, IncrementalRatingPredictor, IncrementalItemRecommender, and UserAverage.
void SaveModel | ( | string | filename | ) | [inherited] |
Save the model parameters to a file.
filename | the name of the file to write to |
Implemented in BPRMF, BPRSLIM, MatrixFactorization, BiasedMatrixFactorization, CoClustering, LeastSquareSLIM, SVDPlusPlus, BPRLinear, UserItemBaseline, FactorWiseMatrixFactorization, BiPolarSlopeOne, SigmoidCombinedAsymmetricFactorModel, NaiveBayes, SigmoidItemAsymmetricFactorModel, SigmoidUserAsymmetricFactorModel, KNN, SlopeOne, KNN, MostPopular, SLIM, Recommender, ItemAttributeSVM, MostPopularByAttributes, MF, EntityAverage, Ensemble, WeightedEnsemble, GlobalAverage, ExternalItemRecommender, ExternalRatingPredictor, Constant, Random, Random, and Zero.
string ToString | ( | ) | [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.
Implemented 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, Recommender, BPRMF_UserMapping_Optimal, BPRMF_ItemMappingKNN, ExternalItemRecommender, ExternalRatingPredictor, WeightedBPRMF, MultiCoreBPRMF, and Constant.
void UpdateRatings | ( | IRatings | ratings | ) |
Update existing ratings and perform incremental training.
ratings | the ratings |
Implemented in MatrixFactorization, UserItemBaseline, NaiveBayes, ItemKNN, UserKNN, UserAverage, GlobalAverage, ItemAverage, and IncrementalRatingPredictor.
float MaxRating [get, set, inherited] |
float MinRating [get, set, inherited] |
the ratings to learn from
Implemented in KNN, FactorWiseMatrixFactorization, TimeAwareRatingPredictor, RatingPredictor, ItemKNN, and UserKNN.
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.
Implemented in IncrementalItemRecommender, and IncrementalRatingPredictor.
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.
Implemented in IncrementalItemRecommender, and IncrementalRatingPredictor.