UserAttributeKNN Class Reference

k-nearest neighbor user-based collaborative filtering using cosine-similarity over the user attibutes More...

Inheritance diagram for UserAttributeKNN:
UserKNN IUserAttributeAwareRecommender KNN IRecommender ItemRecommender IItemRecommender IRecommender

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.
int NumUserAttributes [get, set]
SparseBooleanMatrix UserAttributes [get, set]

Detailed Description

k-nearest neighbor user-based collaborative filtering using cosine-similarity over the user attibutes

This recommender does NOT support incremental updates.


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, inherited]

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 ItemRecommender.

Reimplemented in WeightedUserKNN.

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 UserKNN.


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.


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.

int NumUserAttributes [get, set]

Number of binary user attributes

Implements IUserAttributeAwareRecommender.

SparseBooleanMatrix UserAttributes [get, set]

The binary user attributes

Implements IUserAttributeAwareRecommender.


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