MyMediaLite
3.07
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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, 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.
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