ItemKNN Class Reference

Weighted item-based kNN. More...

Inheritance diagram for ItemKNN:
KNN UserItemBaseline RatingPredictor IIterativeModel IRatingPredictor IRecommender IRecommender ItemAttributeKNN ItemKNNCosine ItemKNNPearson

List of all members.

Public Member Functions

override void AddRating (int user_id, int item_id, double rating)
virtual bool CanPredict (int user_id, int item_id)
 Check whether a useful prediction can be made for a given user-item combination.
Object Clone ()
 create a shallow copy of the object
double ComputeFit ()
 Compute the fit (RMSE) 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 user_id, int item_id)
 Predict the rating of a given user for a given item.
virtual void RemoveItem (int item_id)
override void RemoveRating (int user_id, int item_id)
virtual void RemoveUser (int user_id)
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)

Protected Member Functions

override void AddItem (int item_id)
override void AddUser (int user_id)
override void InitModel ()
 Inits the recommender model.
virtual void RetrainItem (int item_id)
virtual void RetrainUser (int user_id)

Protected Attributes

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.
double max_rating
 The max rating value.
double min_rating
 The min 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]
 The max rating value.
int MaxUserID [get, set]
 Maximum user ID.
virtual double MinRating [get, set]
 The min rating value.
uint NumIter [get, set]
 Number of iterations to run the training.
override IRatings Ratings [set]
 The rating data.
double RegI [get, set]
 Regularization parameter for the item biases.
double RegU [get, set]
 Regularization parameter for the user biases.
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

Detailed Description

Weighted item-based kNN.


Member Function Documentation

virtual bool CanPredict ( int  user_id,
int  item_id 
) [virtual, inherited]

Check whether a useful prediction can be made for a given user-item combination.

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 BiPolarSlopeOne, GlobalAverage, ItemAverage, SlopeOne, and UserAverage.

Object Clone (  )  [inherited]

create a shallow copy of the object

double ComputeFit (  )  [inherited]

Compute the fit (RMSE) on the training data.

Returns:
the fit (RMSE) on the training data according to the optimization criterion; -1 if not implemented

Implements IIterativeModel.

override void InitModel (  )  [protected, virtual, inherited]

Inits the recommender model.

This method is called by the Train() method. When overriding, please call base.InitModel() to get the functions performed in the base class.

Reimplemented from RatingPredictor.

void Iterate (  )  [inherited]

Run one iteration (= pass over the training data).

Implements IIterativeModel.

override void LoadModel ( string  filename  )  [virtual]

Get the model parameters from a file.

Parameters:
filename the name of the file to read from

Reimplemented from KNN.

override double Predict ( int  user_id,
int  item_id 
) [virtual]

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.

Parameters:
user_id the user ID
item_id the item ID
Returns:
the predicted rating

Reimplemented from UserItemBaseline.

override void SaveModel ( string  filename  )  [virtual, inherited]

Save the model parameters to a file.

Parameters:
filename the name of the file to write to

Reimplemented from UserItemBaseline.

override string ToString (  )  [inherited]

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.

Reimplemented in ItemAttributeKNN, ItemKNNCosine, ItemKNNPearson, UserAttributeKNN, UserKNNCosine, and UserKNNPearson.


Member Data Documentation

CorrelationMatrix correlation [protected, inherited]

Correlation matrix over some kind of entity.

Matrix indicating which item was rated by which user.

Func<int, IList<int> > GetPositivelyCorrelatedEntities [protected]

Get positively correlated entities.

double max_rating [protected, inherited]

The max rating value.

double min_rating [protected, inherited]

The min rating value.

IRatings ratings [protected, inherited]

rating data


Property Documentation

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]

The max rating value.

Implements IRatingPredictor.

int MaxUserID [get, set, inherited]

Maximum user ID.

virtual double MinRating [get, set, inherited]

The min rating value.

Implements IRatingPredictor.

uint NumIter [get, set, inherited]

Number of iterations to run the training.

Implements IIterativeModel.

override IRatings Ratings [set]

The rating data.

Reimplemented from RatingPredictor.

double RegI [get, set, inherited]

Regularization parameter for the item biases.

If not set, the recommender will try to find suitable values.

double RegU [get, set, inherited]

Regularization parameter for the user biases.

If not set, the recommender will try to find suitable values.

bool UpdateItems [get, set, inherited]

true if items shall be updated when doing incremental updates

Default is true. Set to false if you do not want any updates to the item model parameters when doing incremental updates.

bool UpdateUsers [get, set, inherited]

true if users shall be updated when doing incremental updates

Default is true. Set to false if you do not want any updates to the user model parameters when doing incremental updates.


The documentation for this class was generated from the following file:
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