Base class for rating predictors that support incremental training. More...
Public Member Functions | |
virtual void | AddRating (int user_id, int item_id, double rating) |
Add a new rating and perform incremental training. | |
virtual bool | CanPredict (int user_id, int item_id) |
Check whether a useful prediction can be made for a given user-item combination. | |
Object | Clone () |
create a shallow copy of the object | |
virtual void | LoadModel (string file) |
Get the model parameters from a file. | |
abstract double | 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 | RemoveRating (int user_id, int item_id) |
Remove an existing rating 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 | UpdateRating (int user_id, int item_id, double rating) |
Update an existing rating and perform incremental training. | |
Protected Member Functions | |
virtual void | AddItem (int item_id) |
virtual void | AddUser (int user_id) |
Protected Attributes | |
double | max_rating |
Maximum rating value. | |
double | min_rating |
Minimum rating value. | |
IRatings | ratings |
rating data | |
Properties | |
int | MaxItemID [get, set] |
Maximum item ID. | |
virtual double | MaxRating [get, set] |
Maximum rating value. | |
int | MaxUserID [get, set] |
Maximum user ID. | |
virtual double | 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.
virtual void AddRating | ( | int | user_id, | |
int | item_id, | |||
double | rating | |||
) | [inline, virtual] |
Add a new rating and perform incremental training.
user_id | the ID of the user who performed the rating | |
item_id | the ID of the rated item | |
rating | the rating value |
Implements IIncrementalRatingPredictor.
Reimplemented in ItemKNN, MatrixFactorization, UserItemBaseline, and UserKNN.
virtual bool CanPredict | ( | int | user_id, | |
int | item_id | |||
) | [inline, virtual, inherited] |
Check whether a useful prediction can be made for a given user-item combination.
user_id | the user ID | |
item_id | the item ID |
Implements IRecommender.
Reimplemented in BiPolarSlopeOne, GlobalAverage, ItemAverage, 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, EntityAverage, FactorWiseMatrixFactorization, GlobalAverage, ItemKNN, KNN, MatrixFactorization, SlopeOne, and UserItemBaseline.
abstract double 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, FactorWiseMatrixFactorization, GlobalAverage, ItemAverage, ItemKNN, MatrixFactorization, SlopeOne, 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, and MatrixFactorization.
virtual void RemoveRating | ( | int | user_id, | |
int | item_id | |||
) | [inline, virtual] |
Remove an existing rating and perform "incremental" training.
user_id | the ID of the user who performed the rating | |
item_id | the ID of the rated item |
Implements IIncrementalRatingPredictor.
Reimplemented in ItemKNN, MatrixFactorization, 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, and MatrixFactorization.
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, EntityAverage, FactorWiseMatrixFactorization, GlobalAverage, KNN, MatrixFactorization, SlopeOne, 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, FactorWiseMatrixFactorization, ItemAttributeKNN, ItemKNNCosine, ItemKNNPearson, MatrixFactorization, TimeAwareBaseline, TimeAwareBaselineWithFrequencies, UserAttributeKNN, UserItemBaseline, UserKNNCosine, and UserKNNPearson.
virtual void UpdateRating | ( | int | user_id, | |
int | item_id, | |||
double | rating | |||
) | [inline, virtual] |
Update an existing rating and perform incremental training.
user_id | the ID of the user who performed the rating | |
item_id | the ID of the rated item | |
rating | the rating value |
Implements IIncrementalRatingPredictor.
Reimplemented in ItemKNN, MatrixFactorization, UserItemBaseline, and UserKNN.
double max_rating [protected, inherited] |
Maximum rating value.
double min_rating [protected, inherited] |
Minimum rating value.
int MaxItemID [get, set, inherited] |
Maximum item ID.
virtual double MaxRating [get, set, inherited] |
Maximum rating value.
Implements IRatingPredictor.
int MaxUserID [get, set, inherited] |
Maximum user ID.
virtual double MinRating [get, set, inherited] |
Minimum rating value.
Implements IRatingPredictor.
The rating data.
Reimplemented in ItemKNN, TimeAwareRatingPredictor, and UserKNN.
bool UpdateItems [get, set, inherited] |
true if items shall be updated when doing incremental updates
Default is true. Set to false if you do not want any updates to the item model parameters when doing incremental updates.
bool UpdateUsers [get, set, inherited] |
true if users shall be updated when doing incremental updates
Default is true. Set to false if you do not want any updates to the user model parameters when doing incremental updates.