WeightedItemKNN Class Reference

Weighted k-nearest neighbor item-based collaborative filtering using cosine similarity. More...

Inheritance diagram for WeightedItemKNN:
ItemKNN KNN IItemSimilarityProvider ItemRecommender 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.
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

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

Weighted k-nearest neighbor item-based collaborative filtering using cosine similarity.

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.

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_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, inherited]

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  )  [inline, virtual, inherited]

Get the model parameters from a file.

Parameters:
filename the name of the file to read from

Implements ItemRecommender.

override float Predict ( int  user_id,
int  item_id 
) [inline, virtual]

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

Reimplemented from ItemKNN.

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

Implements ItemRecommender.

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 ItemKNN.


Member Data Documentation

CorrelationMatrix correlation [protected, inherited]

Correlation matrix over some kind of entity.

uint k = 80 [protected, inherited]

The number of neighbors to take into account for prediction.

int [][] nearest_neighbors [protected, inherited]

Precomputed nearest neighbors.


Property Documentation

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.


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