MyMediaLite  3.09
Public Member Functions | Protected 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:
IncrementalItemRecommender ItemRecommender IIncrementalItemRecommender Recommender IIncrementalRecommender IRecommender ItemKNN UserKNN ItemAttributeKNN UserAttributeKNN

List of all members.

Public Member Functions

virtual void AddFeedback (ICollection< Tuple< int, int >> feedback)
 Add positive feedback events and perform incremental training.
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
 KNN ()
 Default constructor.
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.
IList< Tuple< int, float > > Recommend (int user_id, int n=-1, ICollection< int > ignore_items=null, ICollection< int > candidate_items=null)
 Recommend items for a given user.
virtual
System.Collections.Generic.IList
< Tuple< int, float > > 
Recommend (int user_id, int n=-1, System.Collections.Generic.ICollection< int > ignore_items=null, System.Collections.Generic.ICollection< int > candidate_items=null)
virtual void RemoveFeedback (ICollection< Tuple< int, int >> feedback)
 Remove all feedback events by the given user-item combinations.
virtual void RemoveItem (int item_id)
 Remove all feedback by one item.
virtual void RemoveUser (int user_id)
 Remove all feedback by one user.
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)
void ResizeNearestNeighbors (int new_size)
 Resizes the nearest neighbors list if necessary.
void Update (ICollection< Tuple< int, int >> feedback)
 Update the correlation matrix for the given feedback.

Protected Attributes

IBinaryDataCorrelationMatrix correlation_matrix
 Correlation matrix over some kind of entity, e.g. users or items.
uint k = 80
 The number of neighbors to take into account for prediction.
IList< IList< int > > nearest_neighbors
 Precomputed nearest neighbors.

Properties

float Alpha [get, set]
 Alpha parameter for BidirectionalConditionalProbability.
BinaryCorrelationType Correlation [get, set]
 The kind of correlation to use.
abstract IBooleanMatrix DataMatrix [get]
 data matrix to learn the correlation from
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.
float Q [get, set]
 Exponent to be used for transforming the neighbor's weights.
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
bool Weighted [get, set]
 Gets or sets a value indicating whether this MyMediaLite.ItemRecommendation.KNN is weighted.

Detailed Description

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

See also:
MyMediaLite.ItemRecommendation.KNN

Constructor & Destructor Documentation

KNN ( ) [inline]

Default constructor.


Member Function Documentation

virtual void AddFeedback ( ICollection< Tuple< int, int >>  feedback) [inline, virtual, inherited]

Add positive feedback events and perform incremental training.

Parameters:
feedbackcollection of user id - item id tuples

Implements IIncrementalItemRecommender.

Reimplemented in UserKNN, ItemKNN, MostPopular, and MF.

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.

Reimplemented in ExternalItemRecommender, ExternalRatingPredictor, BiPolarSlopeOne, SlopeOne, Constant, GlobalAverage, UserAverage, ItemAverage, and Random.

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

Reimplemented from Recommender.

abstract float Predict ( int  user_id,
int  item_id 
) [pure virtual, inherited]
IList<Tuple<int, float> > Recommend ( int  user_id,
int  n = -1,
ICollection< int >  ignore_items = null,
ICollection< int >  candidate_items = null 
) [inherited]

Recommend items for a given user.

Parameters:
user_idthe user ID
nthe number of items to recommend, -1 for as many as possible
ignore_itemscollection if items that should not be returned; if null, use empty collection
candidate_itemsthe candidate items to choose from; if null, use all items
Returns:
a sorted list of (item_id, score) tuples

Implemented in WeightedEnsemble, and Ensemble.

virtual void RemoveFeedback ( ICollection< Tuple< int, int >>  feedback) [inline, virtual, inherited]

Remove all feedback events by the given user-item combinations.

Parameters:
feedbackcollection of user id - item id tuples

Implements IIncrementalItemRecommender.

Reimplemented in UserKNN, MostPopular, ItemKNN, and MF.

virtual void RemoveItem ( int  item_id) [inline, virtual, inherited]

Remove all feedback by one item.

Parameters:
item_idthe item ID

Implements IIncrementalRecommender.

Reimplemented in BPRMF, BPRSLIM, LeastSquareSLIM, MF, and MostPopular.

virtual void RemoveUser ( int  user_id) [inline, virtual, inherited]

Remove all feedback by one user.

Parameters:
user_idthe user ID

Implements IIncrementalRecommender.

Reimplemented in LeastSquareSLIM, MF, and MostPopular.

void ResizeNearestNeighbors ( int  new_size) [inline, protected]

Resizes the nearest neighbors list if necessary.

Parameters:
new_sizethe new size
override void SaveModel ( string  filename) [inline, virtual]

Save the model parameters to a file.

Parameters:
filenamethe name of the file to write to

Reimplemented from Recommender.

override string ToString ( ) [inline]

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 Recommender.

void Update ( ICollection< Tuple< int, int >>  feedback) [inline, protected]

Update the correlation matrix for the given feedback.

Parameters:
feedbackthe feedback (user-item tuples)

Member Data Documentation

Correlation matrix over some kind of entity, e.g. users or items.

uint k = 80 [protected]

The number of neighbors to take into account for prediction.

IList<IList<int> > nearest_neighbors [protected]

Precomputed nearest neighbors.


Property Documentation

float Alpha [get, set]

Alpha parameter for BidirectionalConditionalProbability.

The kind of correlation to use.

abstract IBooleanMatrix DataMatrix [get, protected]

data matrix to learn the correlation from

Reimplemented in ItemKNN, UserKNN, ItemAttributeKNN, and UserAttributeKNN.

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.

float Q [get, set]

Exponent to be used for transforming the neighbor's weights.

A value of 0 leads to counting of the relevant neighbors. 1 is the usual weighted prediction. Values greater than 1 give higher weight to higher correlated neighbors.

TODO LIT

bool UpdateItems [get, set, inherited]

true if items shall be updated when doing incremental updates

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

Implements IIncrementalRecommender.

bool UpdateUsers [get, set, inherited]

true if users shall be updated when doing incremental updates

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

Implements IIncrementalRecommender.

bool Weighted [get, set]

Gets or sets a value indicating whether this MyMediaLite.ItemRecommendation.KNN is weighted.

TODO add literature reference


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