Base class for rating predictors that support incremental training. More...
Public Member Functions | |
virtual void | AddRatings (IRatings new_ratings) |
Add new ratings and perform incremental training. | |
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 | |
IncrementalRatingPredictor () | |
Default constructor. | |
virtual 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. | |
virtual void | RemoveItem (int item_id) |
Remove an item from the recommender model, and delete all ratings of this item. | |
virtual void | RemoveRatings (IDataSet ratings_to_delete) |
Remove existing ratings and perform "incremental" training. | |
virtual void | RemoveUser (int user_id) |
Remove a user from the recommender model, and delete all their ratings. | |
virtual void | SaveModel (string file) |
Save the model parameters to a file. | |
override string | ToString () |
Return a string representation of the recommender. | |
abstract void | Train () |
Learn the model parameters of the recommender from the training data. | |
virtual void | UpdateRatings (IRatings new_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] |
true if items shall be updated when doing incremental updates | |
bool | UpdateUsers [get, set] |
true if users shall be updated when doing incremental updates |
Base class for rating predictors that support incremental training.
IncrementalRatingPredictor | ( | ) | [inline] |
Default constructor.
virtual void AddRatings | ( | IRatings | ratings | ) | [inline, virtual] |
Add new ratings and perform incremental training.
ratings | the ratings |
Implements IIncrementalRatingPredictor.
Reimplemented in GlobalAverage, ItemAverage, ItemKNN, MatrixFactorization, UserAverage, UserItemBaseline, and UserKNN.
virtual bool CanPredict | ( | int | user_id, | |
int | item_id | |||
) | [inline, virtual, inherited] |
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 BiPolarSlopeOne, Constant, GlobalAverage, ItemAverage, Random, SlopeOne, and UserAverage.
Object Clone | ( | ) | [inline, inherited] |
create a shallow copy of the object
virtual void LoadModel | ( | string | filename | ) | [inline, virtual, inherited] |
Get the model parameters from a file.
filename | the name of the file to read from |
Implements IRecommender.
Reimplemented in BiasedMatrixFactorization, BiPolarSlopeOne, CoClustering, Constant, EntityAverage, FactorWiseMatrixFactorization, GlobalAverage, ItemKNN, KNN, MatrixFactorization, Random, SigmoidSVDPlusPlus, SlopeOne, SVDPlusPlus, and UserItemBaseline.
abstract float Predict | ( | int | user_id, | |
int | item_id | |||
) | [pure virtual, inherited] |
Predict rating or score for a given user-item combination.
user_id | the user ID | |
item_id | the item ID |
Implements IRecommender.
Implemented in BiasedMatrixFactorization, BiPolarSlopeOne, CoClustering, Constant, FactorWiseMatrixFactorization, GlobalAverage, ItemAverage, ItemKNN, LatentFeatureLogLinearModel, MatrixFactorization, Random, SigmoidSVDPlusPlus, SlopeOne, SVDPlusPlus, TimeAwareBaseline, UserAverage, UserItemBaseline, and UserKNN.
virtual void RemoveItem | ( | int | item_id | ) | [inline, virtual] |
Remove an item from the recommender model, and delete all ratings of this item.
It is up to the recommender implementor whether there should be model updates after this action, both options are valid.
item_id | the ID of the user to be removed |
Implements IIncrementalRatingPredictor.
Reimplemented in BiasedMatrixFactorization, ItemAverage, and MatrixFactorization.
virtual void RemoveRatings | ( | IDataSet | ratings | ) | [inline, virtual] |
Remove existing ratings and perform "incremental" training.
ratings | the user and item IDs of the ratings to be removed |
Implements IIncrementalRatingPredictor.
Reimplemented in GlobalAverage, ItemAverage, ItemKNN, MatrixFactorization, UserAverage, UserItemBaseline, and UserKNN.
virtual void RemoveUser | ( | int | user_id | ) | [inline, virtual] |
Remove a user from the recommender model, and delete all their ratings.
It is up to the recommender implementor whether there should be model updates after this action, both options are valid.
user_id | the ID of the user to be removed |
Implements IIncrementalRatingPredictor.
Reimplemented in BiasedMatrixFactorization, MatrixFactorization, and UserAverage.
virtual void SaveModel | ( | string | filename | ) | [inline, virtual, inherited] |
Save the model parameters to a file.
filename | the name of the file to write to |
Implements IRecommender.
Reimplemented in BiasedMatrixFactorization, BiPolarSlopeOne, CoClustering, Constant, EntityAverage, FactorWiseMatrixFactorization, GlobalAverage, KNN, MatrixFactorization, Random, SlopeOne, SVDPlusPlus, and UserItemBaseline.
override string ToString | ( | ) | [inline, 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.
Implements IRecommender.
Reimplemented in BiasedMatrixFactorization, CoClustering, Constant, FactorWiseMatrixFactorization, ItemAttributeKNN, ItemKNNCosine, ItemKNNPearson, LatentFeatureLogLinearModel, MatrixFactorization, SigmoidSVDPlusPlus, SocialMF, SVDPlusPlus, TimeAwareBaseline, TimeAwareBaselineWithFrequencies, UserAttributeKNN, UserItemBaseline, UserKNNCosine, and UserKNNPearson.
virtual void UpdateRatings | ( | IRatings | ratings | ) | [inline, virtual] |
Update existing ratings and perform incremental training.
ratings | the ratings |
Implements IIncrementalRatingPredictor.
Reimplemented in GlobalAverage, ItemAverage, ItemKNN, MatrixFactorization, UserAverage, UserItemBaseline, and UserKNN.
float max_rating [protected, inherited] |
Maximum rating value.
float min_rating [protected, inherited] |
Minimum rating value.
int MaxItemID [get, set, inherited] |
Maximum item ID.
virtual float MaxRating [get, set, inherited] |
Maximum rating value.
Implements IRatingPredictor.
int MaxUserID [get, set, inherited] |
Maximum user ID.
virtual float MinRating [get, set, inherited] |
Minimum rating value.
Implements IRatingPredictor.
The rating data.
Reimplemented in FactorWiseMatrixFactorization, ItemKNN, KNN, TimeAwareRatingPredictor, and UserKNN.
bool UpdateItems [get, set] |
true if items shall be updated when doing incremental updates
Default should true. Set to false if you do not want any updates to the item model parameters when doing incremental updates.
Implements IIncrementalRatingPredictor.
bool UpdateUsers [get, set] |
true if users shall be updated when doing incremental updates
Default should be true. Set to false if you do not want any updates to the user model parameters when doing incremental updates.
Implements IIncrementalRatingPredictor.