Weighted item-based kNN More...

Inheritance diagram for ItemKNN:

Public Member Functions
override void	AddRatings (IRatings ratings)
	Add new ratings and perform incremental training More...

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 More...

Object	Clone ()
	create a shallow copy of the object More...

float	GetItemSimilarity (int item_id1, int item_id2)
	get the similarity between two items More...

IList< int >	GetMostSimilarItems (int item_id, uint n=10)
	get the most similar items More...

override void	LoadModel (string filename)
	Get the model parameters from a file More...

override float	Predict (int user_id, int item_id)
	Predict the rating of a given user for a given item More...

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 More...

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	RemoveItem (int item_id)
	Remove all feedback by one item More...

override void	RemoveRatings (IDataSet ratings)
	Remove existing ratings and perform "incremental" training More...

virtual void	RemoveUser (int user_id)
	Remove all feedback by one user More...

override void	SaveModel (string filename)
	Save the model parameters to a file More...

IList< Tuple< int, float > >	ScoreItems (IList< Tuple< int, float >> rated_items, IList< int > candidate_items)
	Rate a list of items given a list of ratings that represent a new user More...

override string	ToString ()
	Return a string representation of the recommender More...

override void	Train ()
	Learn the model parameters of the recommender from the training data More...

override void	UpdateRatings (IRatings ratings)
	Update existing ratings and perform incremental training More...

Protected Member Functions
override void	AddItem (int item_id)

virtual void	AddUser (int user_id)

virtual void	RetrainItem (int item_id)
	Retrain model for a given item More...

Protected Attributes
UserItemBaseline	baseline_predictor = new UserItemBaseline()
	underlying baseline predictor More...

ICorrelationMatrix	correlation_matrix
	Correlation matrix over some kind of entity More...

SparseBooleanMatrix	data_item
	Matrix indicating which item was rated by which user More...

float	max_rating
	Maximum rating value More...

float	min_rating
	Minimum rating value More...

IRatings	ratings
	rating data More...

Properties
float	Alpha `[get, set]`
	Alpha parameter for BidirectionalConditionalProbability, or shrinkage parameter for Pearson More...

override IBooleanMatrix	BinaryDataMatrix `[get]`

RatingCorrelationType	Correlation `[get, set]`
	The kind of correlation to use More...

override EntityType	Entity `[get]`

uint	K `[get, set]`
	Number of neighbors to take into account for predictions More...

int	MaxItemID `[get, set]`
	Maximum item ID More...

virtual float	MaxRating `[get, set]`
	Maximum rating value More...

int	MaxUserID `[get, set]`
	Maximum user ID More...

virtual float	MinRating `[get, set]`
	Minimum rating value More...

uint	NumIter `[get, set]`
	number of iterations used for training the underlying baseline predictor More...

override IRatings	Ratings `[set]`

float	RegI `[get, set]`
	regularization constant for the item bias of the underlying baseline predictor More...

float	RegU `[get, set]`
	regularization constant for the user bias of the underlying baseline predictor More...

bool	UpdateItems `[get, set]`

bool	UpdateUsers `[get, set]`

bool	WeightedBinary `[get, set]`
	If set to true, give a lower weight to evidence coming from very frequent entities More...

Detailed Description

Weighted item-based kNN

Member Function Documentation

override void AddRatings ( IRatings ratings )

inlinevirtual

Add new ratings and perform incremental training

Parameters

ratings the ratings

Reimplemented from IncrementalRatingPredictor.

virtual bool CanPredict	(	int	user_id,
		int	item_id
	)

inlinevirtualinherited

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_id	the user ID
item_id	the 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 ( )

inlineinherited

create a shallow copy of the object

float GetItemSimilarity	(	int	item_id1,
		int	item_id2
	)

inline

get the similarity between two items

Returns: the item similarity; higher means more similar

Parameters

item_id1	the ID of the first item
item_id2	the ID of the second item

Implements IItemSimilarityProvider.

IList<int> GetMostSimilarItems	(	int	item_id,
		uint	n = `10`
	)

inline

get the most similar items

Returns: the items most similar to a given item

Parameters

item_id	the ID of the item
n	the number of similar items to return

Implements IItemSimilarityProvider.

override void LoadModel ( string filename )

inlinevirtualinherited

Get the model parameters from a file

Parameters

filename the name of the file to read from

Reimplemented from Recommender.

override float Predict	(	int	user_id,
		int	item_id
	)

inline

Predict the rating of a given user for a given item

If the user or the item are not known to the recommender, a suitable average is returned. To avoid this behavior for unknown entities, use CanPredict() to check before.

Parameters

user_id	the user ID
item_id	the item ID

Returns: the predicted rating

Implements IRecommender.

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_id	the user ID
n	the number of items to recommend, -1 for as many as possible
ignore_items	collection if items that should not be returned; if null, use empty collection
candidate_items	the 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 RemoveItem ( int item_id )

inlinevirtualinherited

Remove all feedback by one item

Parameters

item_id the item ID

Implements IIncrementalRecommender.

Reimplemented in BiasedMatrixFactorization, MatrixFactorization, and ItemAverage.

override void RemoveRatings ( IDataSet ratings )

inlinevirtual

Remove existing ratings and perform "incremental" training

Parameters

ratings the user and item IDs of the ratings to be removed

Reimplemented from IncrementalRatingPredictor.

virtual void RemoveUser ( int user_id )

inlinevirtualinherited

Remove all feedback by one user

Parameters

user_id the user ID

Implements IIncrementalRecommender.

Reimplemented in BiasedMatrixFactorization, MatrixFactorization, and UserAverage.

virtual void RetrainItem ( int item_id )

inlineprotectedvirtual

Retrain model for a given item

Parameters

item_id the item ID

Reimplemented in ItemAttributeKNN.

override void SaveModel ( string filename )

inlinevirtualinherited

Save the model parameters to a file

Parameters

filename the name of the file to write to

Reimplemented from Recommender.

IList<Tuple<int, float> > ScoreItems	(	IList< Tuple< int, float >>	rated_items,
		IList< int >	candidate_items
	)

inline

Rate a list of items given a list of ratings that represent a new user

Returns: a list of int and float pairs, representing item IDs and predicted ratings

Parameters

rated_items	the ratings (item IDs and rating values) representing the new user
candidate_items	the items to be rated

Implements IFoldInRatingPredictor.

override string ToString ( )

inlineinherited

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.

override void Train ( )

inlinevirtualinherited

Learn the model parameters of the recommender from the training data

Implements Recommender.

override void UpdateRatings ( IRatings ratings )

inlinevirtual

Update existing ratings and perform incremental training

Parameters

ratings the ratings

Reimplemented from IncrementalRatingPredictor.

Member Data Documentation

UserItemBaseline baseline_predictor = new UserItemBaseline()

protectedinherited

underlying baseline predictor

ICorrelationMatrix correlation_matrix

protectedinherited

Correlation matrix over some kind of entity

SparseBooleanMatrix data_item

protected

Matrix indicating which item was rated by which user

float max_rating

protectedinherited

Maximum rating value

float min_rating

protectedinherited

Minimum rating value

IRatings ratings

protectedinherited

rating data

Property Documentation

float Alpha

getsetinherited

Alpha parameter for BidirectionalConditionalProbability, or shrinkage parameter for Pearson

RatingCorrelationType Correlation

getsetinherited

The kind of correlation to use

uint K

getsetinherited

Number of neighbors to take into account for predictions

int MaxItemID

getsetinherited

Maximum item ID

virtual float MaxRating

getsetinherited

Maximum rating value

int MaxUserID

getsetinherited

Maximum user ID

virtual float MinRating

getsetinherited

Minimum rating value

uint NumIter

getsetinherited

number of iterations used for training the underlying baseline predictor

float RegI

getsetinherited

regularization constant for the item bias of the underlying baseline predictor

float RegU

getsetinherited

regularization constant for the user bias of the underlying baseline predictor

bool WeightedBinary

getsetinherited

If set to true, give a lower weight to evidence coming from very frequent entities

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

RatingPrediction/ItemKNN.cs

Public Member Functions

Protected Member Functions

Protected Attributes

Properties

Detailed Description

Member Function Documentation

Member Data Documentation

Property Documentation