Weighted kNN recommender based on user attributes. More...
Public Member Functions | |
override 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 (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 double | 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 | 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. | |
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 | 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) |
override void | AddUser (int user_id) |
override void | RetrainUser (int user_id) |
Retrain model for a given user. | |
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_user |
boolean matrix indicating which user rated which item | |
double | max_rating |
Maximum rating value. | |
double | 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 double | MaxRating [get, set] |
Maximum rating value. | |
int | MaxUserID [get, set] |
Maximum user ID. | |
virtual double | MinRating [get, set] |
Minimum rating value. | |
int | NumUserAttributes [get, set] |
override IRatings | Ratings [set] |
The rating data. | |
double | RegI [get, set] |
regularization constant for the item bias of the underlying baseline predictor | |
double | RegU [get, set] |
regularization constant for the user bias of the underlying baseline predictor | |
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 | |
SparseBooleanMatrix | UserAttributes [get, set] |
Weighted kNN recommender based on user attributes.
This recommender does NOT support incremental updates.
override void AddRating | ( | int | user_id, | |
int | item_id, | |||
double | rating | |||
) | [inline, virtual, inherited] |
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 |
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, GlobalAverage, ItemAverage, SlopeOne, and UserAverage.
Object Clone | ( | ) | [inline, inherited] |
create a shallow copy of the object
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 RatingPredictor.
Reimplemented in ItemKNN.
override double 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 rating 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, and MatrixFactorization.
override void RemoveRating | ( | int | user_id, | |
int | item_id | |||
) | [inline, virtual, inherited] |
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 |
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, and MatrixFactorization.
override void RetrainUser | ( | int | user_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.
Implements IRecommender.
override void UpdateRating | ( | int | user_id, | |
int | item_id, | |||
double | rating | |||
) | [inline, virtual, inherited] |
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 |
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_user [protected, inherited] |
boolean matrix indicating which user rated which item
double max_rating [protected, inherited] |
Maximum rating value.
double 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 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.
int NumUserAttributes [get, set] |
Number of binary user attributes
Implements IUserAttributeAwareRecommender.
double RegI [get, set, inherited] |
regularization constant for the item bias of the underlying baseline predictor
double RegU [get, set, inherited] |
regularization constant for the user bias of the underlying baseline predictor
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.
SparseBooleanMatrix UserAttributes [get, set] |
The binary user attributes
Implements IUserAttributeAwareRecommender.