Co-clustering for rating prediction. More...
Public Member Functions | |
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 | |
CoClustering () | |
Default constructor. | |
double | ComputeFit () |
Compute the fit (e.g. RMSE for rating prediction or AUC for item prediction/ranking) on the training data. | |
void | Iterate () |
Run one iteration (= pass over the training data). | |
override void | LoadModel (string filename) |
Get the model parameters from a file. | |
override double | Predict (int u, int i) |
Predict rating or score for a given user-item combination. | |
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. | |
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. | |
int | NumItemClusters [get, set] |
The number of item clusters. | |
uint | NumIter [get, set] |
The maximum number of iterations. | |
int | NumUserClusters [get, set] |
The number of user clusters. | |
virtual IRatings | Ratings [get, set] |
The rating data. |
Co-clustering for rating prediction.
Literature:
This recommender does NOT support incremental updates.
CoClustering | ( | ) | [inline] |
Default constructor.
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
double ComputeFit | ( | ) | [inline] |
Compute the fit (e.g. RMSE for rating prediction or AUC for item prediction/ranking) on the training data.
Implements IIterativeModel.
void Iterate | ( | ) | [inline] |
Run one iteration (= pass over the training data).
Implements IIterativeModel.
override void LoadModel | ( | string | filename | ) | [inline, virtual] |
Get the model parameters from a file.
filename | the name of the file to read from |
Reimplemented from RatingPredictor.
override double Predict | ( | int | user_id, | |
int | item_id | |||
) | [inline, virtual] |
Predict rating or score for a given user-item combination.
user_id | the user ID | |
item_id | the item ID |
Implements RatingPredictor.
override void SaveModel | ( | string | filename | ) | [inline, virtual] |
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.
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.
int NumItemClusters [get, set] |
The number of item clusters.
uint NumIter [get, set] |
The maximum number of iterations.
If the algorithm converges to a stable solution, it will terminate earlier.
Implements IIterativeModel.
int NumUserClusters [get, set] |
The number of user clusters.
The rating data.
Reimplemented in FactorWiseMatrixFactorization, ItemKNN, KNN, TimeAwareRatingPredictor, and UserKNN.