MyMediaLite  3.02
Public Member Functions | Protected Attributes | Properties
KNN Class Reference

Base class for item recommenders that use some kind of k-nearest neighbors (kNN) model. More...

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

List of all members.

Public Member Functions

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.
Object Clone ()
 create a shallow copy of the object
override void LoadModel (string filename)
 Get the model parameters from a file.
abstract float Predict (int user_id, int item_id)
 Predict rating or score for a given user-item combination.
override void SaveModel (string filename)
 Save the model parameters to a file.
override string ToString ()
 Return a string representation of the recommender.
abstract void Train ()
 Learn the model parameters of the recommender from the training data.

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 k-nearest neighbors (kNN) model.

See also:
MyMediaLite.ItemRecommendation.KNN

Member Function Documentation

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

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:
user_idthe user ID
item_idthe item ID
Returns:
true if a useful prediction can be made, false otherwise

Implements IRecommender.

Object Clone ( ) [inline, inherited]

create a shallow copy of the object

override void LoadModel ( string  filename) [inline, virtual]

Get the model parameters from a file.

Parameters:
filenamethe name of the file to read from

Implements ItemRecommender.

abstract float Predict ( int  user_id,
int  item_id 
) [pure virtual, inherited]

Predict rating or score for a given user-item combination.

Parameters:
user_idthe user ID
item_idthe item ID
Returns:
the predicted score/rating for the given user-item combination

Implements IRecommender.

Implemented in BPRMF, BPRMF_ItemMapping, BPRLinear, BPRMF_UserMapping, ItemAttributeSVM, MF, MostPopularByAttributes, MostPopular, UserKNN, ItemKNN, WeightedItemKNN, Random, WeightedUserKNN, and Zero.

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

Save the model parameters to a file.

Parameters:
filenamethe name of the file to write to

Implements ItemRecommender.

override string ToString ( ) [inline, 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 BPRMF, BPRMF_Mapping, BPRLinear, BPRMF_ItemMapping, BPRMF_UserMapping, WRMF, MultiCoreBPRMF, BPRMF_ItemMapping_Optimal, BPRMF_ItemMappingSVR, SoftMarginRankingMF, ItemAttributeSVM, BPRMF_UserMapping_Optimal, BPRMF_ItemMappingKNN, ItemKNN, UserKNN, WeightedBPRMF, ItemAttributeKNN, UserAttributeKNN, WeightedItemKNN, and WeightedUserKNN.


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: