Co-clustering for rating prediction
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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
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Object | Clone () |
| create a shallow copy of the object
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| CoClustering () |
| Default constructor
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float | ComputeObjective () |
| Compute the current optimization objective (usually loss plus regularization term) of the model
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void | Iterate () |
| Run one iteration (= pass over the training data)
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override void | LoadModel (string filename) |
| Get the model parameters from a file
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override float | Predict (int u, int i) |
| Predict rating or score for a given user-item combination
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IList< Tuple< int, float > > | Recommend (int user_id, int n=-1, ICollection< int > ignore_items=null, ICollection< int > candidate_items=null) |
| Recommend items for a given user
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virtual
System.Collections.Generic.IList
< Tuple< int, float > > | Recommend (int user_id, int n=-1, System.Collections.Generic.ICollection< int > ignore_items=null, System.Collections.Generic.ICollection< int > candidate_items=null) |
override void | SaveModel (string filename) |
| Save the model parameters to a file
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override string | ToString () |
| Return a string representation of the recommender
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override void | Train () |
| Learn the model parameters of the recommender from the training data
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Detailed Description
Co-clustering for rating prediction
Literature:
This recommender does NOT support incremental updates.
Constructor & Destructor Documentation
Member Function Documentation
virtual bool CanPredict |
( |
int |
user_id, |
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int |
item_id |
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) |
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inlinevirtualinherited |
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.
- Parameters
-
user_id | the user ID |
item_id | the item ID |
- Returns
- true if a useful prediction can be made, false otherwise
Implements IRecommender.
Reimplemented in ExternalItemRecommender, ExternalRatingPredictor, BiPolarSlopeOne, SlopeOne, Constant, GlobalAverage, UserAverage, ItemAverage, and Random.
create a shallow copy of the object
float ComputeObjective |
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| ) |
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inline |
Compute the current optimization objective (usually loss plus regularization term) of the model
- Returns
- the current objective; -1 if not implemented
Implements IIterativeModel.
Run one iteration (= pass over the training data)
Implements IIterativeModel.
override void LoadModel |
( |
string |
filename | ) |
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inlinevirtual |
Get the model parameters from a file
- Parameters
-
filename | the name of the file to read from |
Reimplemented from Recommender.
override float Predict |
( |
int |
user_id, |
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int |
item_id |
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) |
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inlinevirtual |
Predict rating or score for a given user-item combination
- Parameters
-
user_id | the user ID |
item_id | the item ID |
- Returns
- the predicted score/rating for the given user-item combination
Implements Recommender.
IList<Tuple<int, float> > Recommend |
( |
int |
user_id, |
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int |
n = -1 , |
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ICollection< int > |
ignore_items = null , |
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ICollection< int > |
candidate_items = null |
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) |
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inherited |
Recommend items for a given user
- Parameters
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user_id | the user ID |
n | the number of items to recommend, -1 for as many as possible |
ignore_items | collection if items that should not be returned; if null, use empty collection |
candidate_items | the candidate items to choose from; if null, use all items |
- Returns
- a sorted list of (item_id, score) tuples
Implemented in WeightedEnsemble, and Ensemble.
override void SaveModel |
( |
string |
filename | ) |
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inlinevirtual |
Save the model parameters to a file
- Parameters
-
filename | the name of the file to write to |
Reimplemented from Recommender.
override string ToString |
( |
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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 Recommender.
Learn the model parameters of the recommender from the training data
Implements Recommender.
Member Data Documentation
Property Documentation
The number of item clusters
The maximum number of iterations
If the algorithm converges to a stable solution, it will terminate earlier.
The number of user clusters
The documentation for this class was generated from the following file: