WeightedItemKNN Class Reference

Weighted k-nearest neighbor item-based collaborative filtering using cosine similarity. More...

Inheritance diagram for WeightedItemKNN:

List of all members.

Public Member Functions
virtual void	AddFeedback (int user_id, int item_id)
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
override void	LoadModel (string filename)
	Get the model parameters from a file.
override double	Predict (int user_id, int item_id)
	Predict rating or score for a given user-item combination.
virtual void	RemoveFeedback (int user_id, int item_id)
virtual void	RemoveItem (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.
Protected Member Functions
virtual void	AddItem (int item_id)
virtual void	AddUser (int user_id)
Protected Attributes
CorrelationMatrix	correlation
	Correlation matrix over some kind of entity.
uint	k = 80
	The number of neighbors to take into account for prediction.
int[][]	nearest_neighbors
	Precomputed nearest neighbors.
Properties
virtual IPosOnlyFeedback	Feedback [get, set]
	the feedback data to be used for training
uint	K [get, set]
	The number of neighbors to take into account for prediction.
int	MaxItemID [get, set]
	Maximum item ID.
int	MaxUserID [get, set]
	Maximum user ID.

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Detailed Description

Weighted k-nearest neighbor item-based collaborative filtering using cosine similarity.

This recommender does NOT support incremental updates.

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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.

Object Clone

(

)

[inherited]

create a shallow copy of the object

override void LoadModel

(

string

filename

)

[virtual, inherited]

Get the model parameters from a file.

Parameters:

filename

the name of the file to read from

Implements ItemRecommender.

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

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

Reimplemented from ItemKNN.

override void SaveModel

(

string

filename

)

[virtual, inherited]

Save the model parameters to a file.

Parameters:

filename

the name of the file to write to

Implements ItemRecommender.

override string ToString

(

)

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 ItemKNN.

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Member Data Documentation

CorrelationMatrix correlation [protected, inherited]

Correlation matrix over some kind of entity.

uint k = 80 [protected, inherited]

The number of neighbors to take into account for prediction.

int [][] nearest_neighbors [protected, inherited]

Precomputed nearest neighbors.

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Property Documentation

virtual IPosOnlyFeedback Feedback [get, set, inherited]

the feedback data to be used for training

uint K [get, set, inherited]

The number of neighbors to take into account for prediction.

int MaxItemID [get, set, inherited]

Maximum item ID.

int MaxUserID [get, set, inherited]

Maximum user ID.

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

• WeightedItemKNN.cs