k-nearest neighbor item-based collaborative filtering using cosine-similarity over the item attibutes More...
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 | |
SparseBooleanMatrix | 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] |
k-nearest neighbor item-based collaborative filtering using cosine-similarity over the item attibutes
This recommender does NOT support incremental updates.
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.
user_id | the user ID | |
item_id | the item ID |
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
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
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.
filename | the name of the file to read from |
Implements ItemRecommender.
override float Predict | ( | int | user_id, | |
int | item_id | |||
) | [inline, virtual, inherited] |
Predict rating or score for a given user-item combination.
user_id | the user ID | |
item_id | the item ID |
Implements ItemRecommender.
Reimplemented in WeightedItemKNN.
override void SaveModel | ( | string | filename | ) | [inline, virtual, inherited] |
Save the model parameters to a file.
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.
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.
virtual IPosOnlyFeedback Feedback [get, set, inherited] |
the feedback data to be used for training
SparseBooleanMatrix ItemAttributes [get, set] |
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.