MyMediaLite
3.10
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Constant item recommender for use as experimental baseline. Always predicts a score of zero 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 | |
override void | LoadModel (string filename) |
Get the model parameters from a file | |
override 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 | |
virtual System.Collections.Generic.IList < Tuple< int, float > > | Recommend (int user_id, int n=-1, System.Collections.Generic.ICollection< int > ignore_items=null, System.Collections.Generic.ICollection< int > candidate_items=null) |
override void | SaveModel (string filename) |
Save the model parameters to a file | |
override string | ToString () |
Return a string representation of the recommender | |
override void | Train () |
Learn the model parameters of the recommender from the training data |
Properties | |
virtual IPosOnlyFeedback | Feedback [get, set] |
the feedback data to be used for training | |
int | MaxItemID [get, set] |
Maximum item ID | |
int | MaxUserID [get, set] |
Maximum user ID |
Constant item recommender for use as experimental baseline. Always predicts a score of zero
This recommender can be used for debugging, e.g. to detect non-random orderings in item lists.
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inlinevirtualinherited |
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.
Reimplemented in ExternalItemRecommender, ExternalRatingPredictor, BiPolarSlopeOne, SlopeOne, Constant, GlobalAverage, UserAverage, ItemAverage, and Random.
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inlineinherited |
create a shallow copy of the object
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inlinevirtual |
Get the model parameters from a file
filename | the name of the file to read from |
Reimplemented from Recommender.
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inlinevirtual |
Predict rating or score for a given user-item combination
user_id | the user ID |
item_id | the item ID |
Implements Recommender.
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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.
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inlinevirtual |
Save the model parameters to a file
filename | the name of the file to write to |
Reimplemented from Recommender.
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inlineinherited |
Return a string representation of the recommender
The ToString() method of recommenders should list the class name and all hyperparameters, separated by space characters.
Implements IRecommender.
Reimplemented 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, ExternalItemRecommender, ExternalRatingPredictor, WeightedBPRMF, MultiCoreBPRMF, and Constant.
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inlinevirtual |
Learn the model parameters of the recommender from the training data
Implements Recommender.
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getsetinherited |
the feedback data to be used for training
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getsetinherited |
Maximum item ID
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getsetinherited |
Maximum user ID