KNN Class Reference

Base class for item recommenders that use some kind of kNN model. More...

Inheritance diagram for KNN:
ItemRecommender IItemRecommender IRecommender ItemKNN UserKNN ItemAttributeKNN WeightedItemKNN UserAttributeKNN WeightedUserKNN

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.
abstract 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.
string ToString ()
 Return a string representation of the recommender.
abstract 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.

Detailed Description

Base class for item recommenders that use some kind of kNN model.


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]

Get the model parameters from a file.

Parameters:
filename the name of the file to read from

Implements ItemRecommender.

abstract double Predict ( int  user_id,
int  item_id 
) [pure 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 IRecommender.

Implemented in BPR_Linear, BPRMF, ItemKNN, MF, MostPopular, Random, UserKNN, WeightedItemKNN, WeightedUserKNN, and Zero.

override void SaveModel ( string  filename  )  [virtual]

Save the model parameters to a file.

Parameters:
filename the name of the file to write to

Implements ItemRecommender.

string ToString (  )  [inherited]

Member Data Documentation

Correlation matrix over some kind of entity.

uint k = 80 [protected]

The number of neighbors to take into account for prediction.

int [][] nearest_neighbors [protected]

Precomputed nearest neighbors.


Property Documentation

virtual IPosOnlyFeedback Feedback [get, set, inherited]

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, inherited]

Maximum item ID.

int MaxUserID [get, set, inherited]

Maximum user ID.


The documentation for this class was generated from the following file:
Generated on Wed Jun 22 20:55:52 2011 for MyMediaLite by  doxygen 1.6.3