MyMediaLite
3.02
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k-nearest neighbor user-based collaborative filtering using cosine-similarity over the user 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 | |
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. | |
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. | |
int | NumUserAttributes [get, set] |
SparseBooleanMatrix | UserAttributes [get, set] |
k-nearest neighbor user-based collaborative filtering using cosine-similarity over the user 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
IList<int> GetMostSimilarUsers | ( | int | user_id, |
uint | n = 10 |
||
) | [inline, inherited] |
get the most similar users
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
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.
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 WeightedUserKNN.
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 UserKNN.
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
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.
SparseBooleanMatrix UserAttributes [get, set] |
The binary user attributes
Implements IUserAttributeAwareRecommender.