MyMediaLite
3.11
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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 More... | |
Object | Clone () |
create a shallow copy of the object More... | |
abstract void | LoadModel (string file) |
Get the model parameters from a file More... | |
abstract float | Predict (int user_id, int item_id) |
Predict rating or score for a given user-item combination More... | |
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 More... | |
abstract void | SaveModel (string file) |
Save the model parameters to a file More... | |
string | ToString () |
Return a string representation of the recommender More... | |
virtual void | Train () |
Learn the model parameters of the recommender from the training data More... | |
Public Attributes | |
IList< IRecommender > | recommenders = new List<IRecommender>() |
list of recommenders More... | |
Properties | |
float | MaxRating [get, set] |
The max rating value More... | |
float | MinRating [get, set] |
The min rating value More... | |
Abtract class for combining several prediction methods
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inlinevirtual |
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.
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inline |
create a shallow copy of the object
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pure virtual |
Get the model parameters from a file
filename | the name of the file to read from |
Implements IRecommender.
Implemented in WeightedEnsemble.
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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.
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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.
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pure virtual |
Save the model parameters to a file
filename | the name of the file to write to |
Implements IRecommender.
Implemented in WeightedEnsemble.
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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, NaiveBayes, WRMF, KNN, KNN, MostPopular, TimeAwareBaselineWithFrequencies, SoftMarginRankingMF, Recommender, ExternalItemRecommender, ExternalRatingPredictor, WeightedBPRMF, MultiCoreBPRMF, and Constant.
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inlinevirtual |
Learn the model parameters of the recommender from the training data
Implements IRecommender.
Reimplemented in WeightedEnsemble.
IList<IRecommender> recommenders = new List<IRecommender>() |
list of recommenders
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getset |
The max rating value
The max rating value
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getset |
The min rating value
The min rating value