Weighted item-based kNN with pearson correlation. More...
Public Member Functions | |
override void | AddRatings (IRatings 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 | |
float | GetItemSimilarity (int item_id1, int item_id2) |
get the similarity between two items | |
IList< int > | GetMostSimilarItems (int item_id, uint n=10) |
get the most similar items | |
override void | LoadModel (string filename) |
Get the model parameters from a file. | |
override float | Predict (int user_id, int item_id) |
Predict the rating of a given user for a given item. | |
virtual void | RemoveItem (int item_id) |
Remove an item from the recommender model, and delete all ratings of this item. | |
override void | RemoveRatings (IDataSet ratings) |
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. | |
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 | |
override void | AddItem (int item_id) |
virtual void | AddUser (int user_id) |
override void | RetrainItem (int item_id) |
Retrain model for a given item. | |
Protected Attributes | |
UserItemBaseline | baseline_predictor = new UserItemBaseline() { RegU = 10, RegI = 5 } |
underlying baseline predictor | |
CorrelationMatrix | correlation |
Correlation matrix over some kind of entity. | |
SparseBooleanMatrix | data_item |
Matrix indicating which item was rated by which user. | |
Func< int, IList< int > > | GetPositivelyCorrelatedEntities |
Get positively correlated entities. | |
float | max_rating |
Maximum rating value. | |
float | min_rating |
Minimum rating value. | |
IRatings | ratings |
rating data | |
Properties | |
uint | K [get, set] |
Number of neighbors to take into account for predictions. | |
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. | |
override IRatings | Ratings [set] |
The rating data. | |
float | RegI [get, set] |
regularization constant for the item bias of the underlying baseline predictor | |
float | RegU [get, set] |
regularization constant for the user bias of the underlying baseline predictor | |
float | Shrinkage [get, set] |
shrinkage (regularization) parameter | |
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 |
Weighted item-based kNN with pearson correlation.
This recommender supports incremental updates.
override void AddRatings | ( | IRatings | ratings | ) | [inline, virtual, inherited] |
Add new ratings and perform incremental training.
ratings | the ratings |
Reimplemented from IncrementalRatingPredictor.
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
float GetItemSimilarity | ( | int | item_id1, | |
int | item_id2 | |||
) | [inline, inherited] |
get the similarity between two items
item_id1 | the ID of the first item | |
item_id2 | the ID of the second item |
Implements IItemSimilarityProvider.
IList<int> GetMostSimilarItems | ( | int | item_id, | |
uint | n = 10 | |||
) | [inline, inherited] |
get the most similar items
item_id | the ID of the item | |
n | the number of similar items to return |
Implements IItemSimilarityProvider.
override void LoadModel | ( | string | filename | ) | [inline, virtual, inherited] |
Get the model parameters from a file.
filename | the name of the file to read from |
Reimplemented from KNN.
override float Predict | ( | int | user_id, | |
int | item_id | |||
) | [inline, virtual, inherited] |
Predict the rating of a given user for a given item.
If the user or the item are not known to the recommender, a suitable average is returned. To avoid this behavior for unknown entities, use CanPredict() to check before.
user_id | the user ID | |
item_id | the item ID |
Implements RatingPredictor.
virtual void RemoveItem | ( | int | item_id | ) | [inline, virtual, inherited] |
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.
override void RemoveRatings | ( | IDataSet | ratings | ) | [inline, virtual, inherited] |
Remove existing ratings and perform "incremental" training.
ratings | the user and item IDs of the ratings to be removed |
Reimplemented from IncrementalRatingPredictor.
virtual void RemoveUser | ( | int | user_id | ) | [inline, virtual, inherited] |
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.
override void RetrainItem | ( | int | item_id | ) | [inline, protected, virtual] |
override void SaveModel | ( | string | filename | ) | [inline, virtual, inherited] |
Save the model parameters to a file.
filename | the name of the file to write to |
Reimplemented from RatingPredictor.
override string ToString | ( | ) | [inline] |
Return a string representation of the recommender.
The ToString() method of recommenders should list the class name and all hyperparameters, separated by space characters.
Reimplemented from RatingPredictor.
override void UpdateRatings | ( | IRatings | ratings | ) | [inline, virtual, inherited] |
Update existing ratings and perform incremental training.
ratings | the ratings |
Reimplemented from IncrementalRatingPredictor.
UserItemBaseline baseline_predictor = new UserItemBaseline() { RegU = 10, RegI = 5 } [protected, inherited] |
underlying baseline predictor
CorrelationMatrix correlation [protected, inherited] |
Correlation matrix over some kind of entity.
SparseBooleanMatrix data_item [protected, inherited] |
Matrix indicating which item was rated by which user.
Func<int, IList<int> > GetPositivelyCorrelatedEntities [protected, inherited] |
Get positively correlated entities.
float max_rating [protected, inherited] |
Maximum rating value.
float min_rating [protected, inherited] |
Minimum rating value.
uint K [get, set, inherited] |
Number of neighbors to take into account for predictions.
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.
float RegI [get, set, inherited] |
regularization constant for the item bias of the underlying baseline predictor
float RegU [get, set, inherited] |
regularization constant for the user bias of the underlying baseline predictor
float Shrinkage [get, set] |
shrinkage (regularization) parameter
bool UpdateItems [get, set, inherited] |
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, inherited] |
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.