Abstract class for SLIM based item predictors proposed by Ning and Karypis
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virtual void | AddFeedback (ICollection< Tuple< int, int >> feedback) |
| Add positive feedback events and perform incremental training More...
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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 More...
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Object | Clone () |
| create a shallow copy of the object More...
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abstract float | ComputeObjective () |
| Compute the current optimization objective (usually loss plus regularization term) of the model More...
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abstract void | Iterate () |
| Iterate once over the data More...
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override void | LoadModel (string file) |
| Get the model parameters from a file More...
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override float | Predict (int user_id, int item_id) |
| Predict rating or score for a given user-item combination More...
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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 More...
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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) |
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virtual void | RemoveFeedback (ICollection< Tuple< int, int >> feedback) |
| Remove all feedback events by the given user-item combinations More...
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virtual void | RemoveItem (int item_id) |
| Remove all feedback by one item More...
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virtual void | RemoveUser (int user_id) |
| Remove all feedback by one user More...
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override void | SaveModel (string file) |
| Save the model parameters to a file More...
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| SLIM () |
| Default constructor More...
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override string | ToString () |
| Return a string representation of the recommender More...
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override void | Train () |
| Learn the model parameters of the recommender from the training data More...
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virtual void | AddItem (int item_id) |
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virtual void | AddUser (int user_id) |
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virtual void | InitModel () |
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virtual IPosOnlyFeedback | Feedback [get, set] |
| the feedback data to be used for training More...
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double | InitMean [get, set] |
| Mean of the normal distribution used to initialize the latent factors More...
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double | InitStdDev [get, set] |
| Standard deviation of the normal distribution used to initialize the latent factors More...
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int | MaxItemID [get, set] |
| Maximum item ID More...
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int | MaxUserID [get, set] |
| Maximum user ID More...
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uint | NumIter [get, set] |
| Number of iterations over the training data More...
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bool | UpdateItems [get, set] |
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bool | UpdateUsers [get, set] |
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Abstract class for SLIM based item predictors proposed by Ning and Karypis
This class only implements the prediction model presented in the original paper.
Literature:
virtual void AddFeedback |
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ICollection< Tuple< int, int >> |
feedback | ) |
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inlinevirtualinherited |
virtual bool CanPredict |
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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
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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
abstract float ComputeObjective |
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pure virtual |
Compute the current optimization objective (usually loss plus regularization term) of the model
- Returns
- the current objective; -1 if not implemented
Implements IIterativeModel.
Implemented in BPRSLIM, and LeastSquareSLIM.
abstract void Iterate |
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pure virtual |
override void LoadModel |
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string |
filename | ) |
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inlinevirtual |
Get the model parameters from a file
- Parameters
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filename | the name of the file to read from |
Reimplemented from Recommender.
override float Predict |
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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
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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 |
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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.
virtual void RemoveFeedback |
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ICollection< Tuple< int, int >> |
feedback | ) |
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inlinevirtualinherited |
virtual void RemoveItem |
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int |
item_id | ) |
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inlinevirtualinherited |
virtual void RemoveUser |
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int |
user_id | ) |
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inlinevirtualinherited |
override void SaveModel |
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string |
filename | ) |
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inlinevirtual |
Save the model parameters to a file
- Parameters
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filename | the name of the file to write to |
Reimplemented from Recommender.
override string ToString |
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inlineinherited |
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.
Learn the model parameters of the recommender from the training data
Implements Recommender.
Item weight matrix (the W matrix in the original paper)
The item KNN used in the feature selection step
the feedback data to be used for training
Mean of the normal distribution used to initialize the latent factors
Standard deviation of the normal distribution used to initialize the latent factors
Number of iterations over the training data
The documentation for this class was generated from the following file: