MyMediaLite
3.08
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Rating predictor that knows beforehand what it will have to rate. More...
Public Member Functions | |
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 | 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. | |
Properties | |
IDataSet | AdditionalFeedback [get, set] |
user-item combinations that are known to be queried | |
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 |
Rating predictor that knows beforehand what it will have to rate.
This is not so interesting for real-world use, but it useful for rating prediction competitions like the Netflix Prize.
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, MatrixFactorization, BiasedMatrixFactorization, BPRSLIM, CoClustering, LeastSquareSLIM, SVDPlusPlus, UserItemBaseline, FactorWiseMatrixFactorization, BPRLinear, SigmoidCombinedAsymmetricFactorModel, MF, SigmoidSVDPlusPlus, BiPolarSlopeOne, SigmoidItemAsymmetricFactorModel, SigmoidUserAsymmetricFactorModel, KNN, KNN, MostPopular, NaiveBayes, SlopeOne, SLIM, MostPopularByAttributes, 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, FactorWiseMatrixFactorization, BPRLinear, GSVDPlusPlus, MF, UserItemBaseline, CoClustering, NaiveBayes, SVDPlusPlus, SLIM, SigmoidCombinedAsymmetricFactorModel, 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 , |
||
ICollection< int > | ignore_items = null , |
||
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 SaveModel | ( | string | filename | ) | [inherited] |
Save the model parameters to a file.
filename | the name of the file to write to |
Implemented in BPRMF, MatrixFactorization, BiasedMatrixFactorization, BPRSLIM, CoClustering, LeastSquareSLIM, SVDPlusPlus, UserItemBaseline, FactorWiseMatrixFactorization, BPRLinear, BiPolarSlopeOne, SigmoidCombinedAsymmetricFactorModel, MF, NaiveBayes, SigmoidItemAsymmetricFactorModel, SigmoidUserAsymmetricFactorModel, SlopeOne, KNN, KNN, MostPopular, SLIM, Recommender, MostPopularByAttributes, 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, SVDPlusPlus, MatrixFactorization, SigmoidCombinedAsymmetricFactorModel, CoClustering, BPRSLIM, SigmoidItemAsymmetricFactorModel, LeastSquareSLIM, TimeAwareBaseline, SigmoidUserAsymmetricFactorModel, LatentFeatureLogLinearModel, FactorWiseMatrixFactorization, UserItemBaseline, SigmoidSVDPlusPlus, SocialMF, BPRLinear, KNN, NaiveBayes, WRMF, KNN, MostPopular, TimeAwareBaselineWithFrequencies, SoftMarginRankingMF, Recommender, ExternalItemRecommender, ExternalRatingPredictor, WeightedBPRMF, MultiCoreBPRMF, and Constant.
IDataSet AdditionalFeedback [get, set] |
user-item combinations that are known to be queried
Implemented in SigmoidCombinedAsymmetricFactorModel, SVDPlusPlus, SigmoidItemAsymmetricFactorModel, and SigmoidUserAsymmetricFactorModel.
float MaxRating [get, set, inherited] |
float MinRating [get, set, inherited] |
the ratings to learn from
Implemented in KNN, FactorWiseMatrixFactorization, TimeAwareRatingPredictor, RatingPredictor, ItemKNN, and UserKNN.