MyMediaLite
3.09
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Abtract class for combining several prediction methods. More...
Public Member Functions | |
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 void | LoadModel (string file) |
Get the model parameters from a file. | |
abstract float | Predict (int user_id, int item_id) |
Predict rating or score for a given user-item combination. | |
abstract IList< Tuple< int, float > > | Recommend (int user_id, int n=20, ICollection< int > ignore_items=null, ICollection< int > candidate_items=null) |
Recommend items for a given user. | |
abstract void | SaveModel (string file) |
Save the model parameters to a file. | |
string | ToString () |
Return a string representation of the recommender. | |
virtual void | Train () |
Learn the model parameters of the recommender from the training data. | |
Public Attributes | |
IList< IRecommender > | recommenders = new List<IRecommender>() |
list of recommenders | |
Properties | |
float | MaxRating [get, set] |
The max rating value. | |
float | MinRating [get, set] |
The min rating value. |
Abtract class for combining several prediction methods.
virtual bool CanPredict | ( | int | user_id, |
int | item_id | ||
) | [inline, virtual] |
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.
Object Clone | ( | ) | [inline] |
create a shallow copy of the object
abstract void LoadModel | ( | string | filename | ) | [pure virtual] |
Get the model parameters from a file.
filename | the name of the file to read from |
Implements IRecommender.
Implemented in WeightedEnsemble.
abstract float Predict | ( | int | user_id, |
int | item_id | ||
) | [pure virtual] |
Predict rating or score for a given user-item combination.
user_id | the user ID |
item_id | the item ID |
Implements IRecommender.
Implemented in WeightedEnsemble.
abstract IList<Tuple<int, float> > Recommend | ( | int | user_id, |
int | n = 20 , |
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ICollection< int > | ignore_items = null , |
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ICollection< int > | candidate_items = null |
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) | [pure virtual] |
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 |
Implements IRecommender.
Implemented in WeightedEnsemble.
abstract void SaveModel | ( | string | filename | ) | [pure virtual] |
Save the model parameters to a file.
filename | the name of the file to write to |
Implements IRecommender.
Implemented in WeightedEnsemble.
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.
IList<IRecommender> recommenders = new List<IRecommender>() |
list of recommenders
float MaxRating [get, set] |
The max rating value.
The max rating value
float MinRating [get, set] |
The min rating value.
The min rating value