MyMediaLite  3.03
Public Member Functions | Protected Attributes | Properties
ItemAttributeKNN Class Reference

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

Inheritance diagram for ItemAttributeKNN:
ItemKNN IItemAttributeAwareRecommender KNN IItemSimilarityProvider IRecommender ItemRecommender Recommender IRecommender

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
float GetItemSimilarity (int item_id1, int item_id2)
 get the similarity between two items
IList< int > GetMostSimilarItems (int item_id, uint n=10)
 get the most similar items
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.
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 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
IBooleanMatrix ItemAttributes [get, set]
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 NumItemAttributes [get, set]
float Q [get, set]
 Exponent to be used for transforming the neighbor's weights.
bool Weighted [get, set]
 Gets or sets a value indicating whether this MyMediaLite.ItemRecommendation.KNN is weighted.

Detailed Description

k-nearest neighbor (kNN) item-based collaborative filtering using the correlation of the item 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_idthe user ID
item_idthe 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

float GetItemSimilarity ( int  item_id1,
int  item_id2 
) [inline, inherited]

get the similarity between two items

Returns:
the item similarity; higher means more similar
Parameters:
item_id1the ID of the first item
item_id2the ID of the second item

Implements IItemSimilarityProvider.

IList<int> GetMostSimilarItems ( int  item_id,
uint  n = 10 
) [inline, inherited]

get the most similar items

Returns:
the items most similar to a given item
Parameters:
item_idthe ID of the item
nthe number of similar items to return

Implements IItemSimilarityProvider.

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

Get the model parameters from a file.

Parameters:
filenamethe name of the file to read from

Reimplemented from Recommender.

override float Predict ( int  user_id,
int  item_id 
) [inline, 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 Recommender.

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.

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

Save the model parameters to a file.

Parameters:
filenamethe name of the file to write to

Reimplemented from Recommender.

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 ItemKNN.

virtual IPosOnlyFeedback Feedback [get, set, inherited]

the feedback data to be used for training

the binary item attributes

Implements IItemAttributeAwareRecommender.

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 NumItemAttributes [get, set]

an integer stating the number of attributes

Implements IItemAttributeAwareRecommender.

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

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: