k-nearest neighbor (kNN) user-based collaborative filtering using the correlation of the user attibutes More...

Inheritance diagram for UserAttributeKNN:

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
IList< int >	GetMostSimilarUsers (int user_id, uint n=10)
	get the most similar users
float	GetUserSimilarity (int user_id1, int user_id2)
	get the similarity between two users
override void	LoadModel (string filename)
	Get the model parameters from a file.
override 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)
override void	SaveModel (string filename)
	Save the model parameters to a file.
IList< Tuple< int, float > >	ScoreItems (IList< int > accessed_items, IList< int > candidate_items)
	Score a list of items given a list of items that represent a new user.
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 IList< float >	FoldIn (IList< int > items)
	Fold in one user, identified by their items.
Protected Attributes
IBinaryDataCorrelationMatrix	correlation
	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.
override 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.
int	NumUserAttributes `[get, set]`
float	Q `[get, set]`
	Exponent to be used for transforming the neighbor's weights.
IBooleanMatrix	UserAttributes `[get, set]`
bool	Weighted `[get, set]`
	Gets or sets a value indicating whether this MyMediaLite.ItemRecommendation.KNN is weighted.

Detailed Description

k-nearest neighbor (kNN) user-based collaborative filtering using the correlation of the user attibutes

This recommender does NOT support incremental updates.

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_id	the user ID
item_id	the item ID

Returns:: true if a useful prediction can be made, false otherwise

Implements IRecommender.

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

Object Clone ( ) [inline, inherited]

create a shallow copy of the object

virtual IList<float> FoldIn ( IList< int > items ) [inline, protected, virtual, inherited]

Fold in one user, identified by their items.

Returns:: a vector containing the similarities to all users

Parameters:

items the items representing the user

IList<int> GetMostSimilarUsers	(	int	user_id,
		uint	n = `10`
	)		`[inline, inherited]`

get the most similar users

Returns:: the users most similar to a given user

Parameters:

user_id	the ID of the user
n	the number of similar users to return

Implements IUserSimilarityProvider.

float GetUserSimilarity	(	int	user_id1,
		int	user_id2
	)		`[inline, inherited]`

get the similarity between two users

Returns:: the user similarity; higher means more similar

Parameters:

user_id1	the ID of the first user
user_id2	the ID of the second user

Implements IUserSimilarityProvider.

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

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, 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.

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.

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

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< int >	accessed_items,
		IList< int >	candidate_items
	)		`[inline, inherited]`

Score a list of items given a list of items that represent a new user.

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

Parameters:

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

Implements IFoldInItemRecommender.

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.

Reimplemented from Recommender.

Member Data Documentation

IBinaryDataCorrelationMatrix correlation [protected, inherited]

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

uint k = 80 [protected, inherited]

The number of neighbors to take into account for prediction.

IList<IList<int> > nearest_neighbors [protected, inherited]

Precomputed nearest neighbors.

Property Documentation

float Alpha [get, set, inherited]

Alpha parameter for BidirectionalConditionalProbability.

BinaryCorrelationType Correlation [get, set, inherited]

The kind of correlation to use.

override IBooleanMatrix DataMatrix [get, protected]

data matrix to learn the correlation from

Reimplemented from UserKNN.

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.

float Q [get, set, inherited]

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

IBooleanMatrix UserAttributes [get, set]

The binary user attributes

Implements IUserAttributeAwareRecommender.

bool Weighted [get, set, inherited]

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:

ItemRecommendation/UserAttributeKNN.cs

Public Member Functions

Protected Member Functions

Protected Attributes

Properties

Detailed Description

Member Function Documentation

Member Data Documentation

Property Documentation