MyMediaLite
3.10
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Uses the average rating value over all ratings for prediction More...
Public Member Functions | |
override void | AddRatings (IRatings ratings) |
Add new ratings and perform incremental training | |
override 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) |
virtual void | RemoveItem (int item_id) |
Remove all feedback by one item | |
override void | RemoveRatings (IDataSet ratings) |
Remove existing ratings and perform "incremental" training | |
virtual void | RemoveUser (int user_id) |
Remove all feedback by one user | |
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 | |
override void | UpdateRatings (IRatings ratings) |
Update existing ratings and perform incremental training |
Protected Member Functions | |
virtual void | AddItem (int item_id) |
virtual void | AddUser (int user_id) |
Protected Attributes | |
float | max_rating |
Maximum rating value | |
float | min_rating |
Minimum rating value | |
IRatings | ratings |
rating data |
Properties | |
int | MaxItemID [get, set] |
Maximum item ID | |
virtual float | MaxRating [get, set] |
Maximum rating value | |
int | MaxUserID [get, set] |
Maximum user ID | |
virtual float | MinRating [get, set] |
Minimum rating value | |
virtual IRatings | Ratings [get, set] |
The rating data | |
bool | UpdateItems [get, set] |
bool | UpdateUsers [get, set] |
Uses the average rating value over all ratings for prediction
This recommender supports incremental updates.
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inlinevirtual |
Add new ratings and perform incremental training
ratings | the ratings |
Reimplemented from IncrementalRatingPredictor.
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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 |
Reimplemented from Recommender.
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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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inlinevirtualinherited |
Remove all feedback by one item
item_id | the item ID |
Implements IIncrementalRecommender.
Reimplemented in BiasedMatrixFactorization, MatrixFactorization, and ItemAverage.
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inlinevirtual |
Remove existing ratings and perform "incremental" training
ratings | the user and item IDs of the ratings to be removed |
Reimplemented from IncrementalRatingPredictor.
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inlinevirtualinherited |
Remove all feedback by one user
user_id | the user ID |
Implements IIncrementalRecommender.
Reimplemented in BiasedMatrixFactorization, MatrixFactorization, and UserAverage.
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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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inlinevirtual |
Update existing ratings and perform incremental training
ratings | the ratings |
Reimplemented from IncrementalRatingPredictor.
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protectedinherited |
Maximum rating value
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protectedinherited |
Minimum rating value
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protectedinherited |
rating data
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getsetinherited |
Maximum item ID
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getsetinherited |
Maximum rating value
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getsetinherited |
Maximum user ID
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getsetinherited |
Minimum rating value