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CFEngine API | ||||||||||
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Packages that use CFNotImplementedException | |
org.recommender.algorithms | A wide variety of different implementations of collaborative filtering recommendation and precition algorithms are provided. |
org.recommender.algorithms.Experimental |
Uses of CFNotImplementedException in org.recommender.algorithms |
Methods in org.recommender.algorithms that throw CFNotImplementedException | |
ItemPrediction[] |
SimplePearsonAlgorithm.getRecommendationsByType(int activeUser,
int n,
int type)
Yun Wang Return top n recommendations of specific type. |
ItemPrediction[] |
SimplePearsonAlgorithm.getRecommendations(int activeUser,
int n)
Return top n recommendations. |
ItemPrediction |
SimplePearsonAlgorithm.predictRating(int activeUser,
int item)
Predicts the rating for the given user and item. |
long |
SimplePearsonAlgorithm.getAveragePredictionTime()
Return average prediction time. |
void |
SimplePearsonAlgorithm.updateUser(int userID)
Doesn't currently need upateUser. |
ItemPrediction |
CFAlgorithm.predictRating(int userID,
int itemID)
Asks the prediction engine to return a prediction for how a particular user will rate a particular item. |
ItemPrediction[] |
CFAlgorithm.getRecommendations(int activeUser,
int n)
Return top n recommendations for the active user. |
long |
CFAlgorithm.getAveragePredictionTime()
Very useful to get the prediction time after a bunch of experiment Should be implemented within each algorithm class. |
void |
CFAlgorithm.updateUser(int userID)
Notify algorithm that the given user's ratings have changed. |
ItemPrediction[] |
CFAlgorithm.getRecommendationsByType(int activeUser,
int n,
int type)
Added by Yun Wang Return top n recommendations for the active user of specific type. |
Uses of CFNotImplementedException in org.recommender.algorithms.Experimental |
Methods in org.recommender.algorithms.Experimental that throw CFNotImplementedException | |
ItemRating[] |
UserItem2Algorithm.getRecommendations(int activeUser,
int n)
Not yet implemented. |
ItemPrediction[] |
UserItem2Algorithm.getRecommendationsByType(int activeUser,
int n,
int type)
Not yet complete |
ItemPrediction |
UserItem2Algorithm.predictRating(int activeUser,
int item)
predicts the rating for the given user and item |
void |
UserItem2Algorithm.updateUser(int userID)
Not yet implemented. |
long |
UserItem2Algorithm.getAveragePredictionTime()
|
ItemPrediction[] |
TransNeighborAlgorithm.getRecommendationsByType(int activeUser,
int n,
int type)
Not yet complete |
ItemPrediction[] |
TransNeighborAlgorithm.getRecommendations(int activeUser,
int n)
Returns top n recommendations. |
ItemPrediction |
TransNeighborAlgorithm.predictRating(int activeUser,
int item)
predicts the rating for the given user and item. |
void |
TransNeighborAlgorithm.updateUser(int userID)
Not yet implemented. |
long |
TransNeighborAlgorithm.getAveragePredictionTime()
Return average prediction time. |
ItemPrediction[] |
SimpleSVD.getRecommendationsByType(int activeUser,
int n,
int type)
Not implemented |
ItemPrediction[] |
SimpleSVD.getRecommendations(int activeUser,
int n)
Not implemented |
ItemPrediction |
SimpleSVD.predictRating(int activeUser,
int item)
Simply return the rating in the SVD-reduced matrix |
void |
SimpleSVD.updateUser(int userID)
Not implemented |
long |
SimpleSVD.getAveragePredictionTime()
Not implemented |
ItemPrediction[] |
SVD_Pearson.getRecommendationsByType(int activeUser,
int n,
int type)
Not implemented |
ItemPrediction[] |
SVD_Pearson.getRecommendations(int activeUser,
int n)
Not implemented |
ItemPrediction |
SVD_Pearson.predictRating(int activeUser,
int item)
Predict ratings using nearest neighbor algorithm -- user correlations are based on the Pearson correlations between users' ratings on "features" |
void |
SVD_Pearson.updateUser(int userID)
Not implemented |
long |
SVD_Pearson.getAveragePredictionTime()
Not implemented |
ItemPrediction[] |
SVD_Cosine.getRecommendationsByType(int activeUser,
int n,
int type)
Not implemented |
ItemPrediction[] |
SVD_Cosine.getRecommendations(int activeUser,
int n)
Not implemented |
ItemPrediction |
SVD_Cosine.predictRating(int activeUser,
int item)
Predict ratings using nearest neighbor algorithm -- user correlations are based on the Cosine distance between users' ratings on "features" |
void |
SVD_Cosine.updateUser(int userID)
Not implemented |
long |
SVD_Cosine.getAveragePredictionTime()
Not implemented |
ItemRating[] |
ContinuousBayesNetAlgorithm.getRecommendations(int activeUser,
int n)
|
ItemRating[] |
ContinuousBayesNetAlgorithm.getRecommendationsByType(int activeUser,
int n,
int type)
|
ItemPrediction |
ContinuousBayesNetAlgorithm.predictRating(int activeUser,
int item)
predicts the rating for the given user and item. |
void |
ContinuousBayesNetAlgorithm.updateUser(int userID)
|
long |
ContinuousBayesNetAlgorithm.getAveragePredictionTime()
|
ItemRating[] |
BayesNetAlgorithm.getRecommendations(int activeUser,
int n)
Returns top n recommendations Not yet complete |
ItemRating[] |
BayesNetAlgorithm.getRecommendationsByType(int activeUser,
int n,
int type)
|
ItemPrediction |
BayesNetAlgorithm.predictRating(int activeUser,
int item)
predicts the rating for the given user and item. |
void |
BayesNetAlgorithm.updateUser(int userID)
Update user information, not yet complete. |
long |
BayesNetAlgorithm.getAveragePredictionTime()
Return average prediction time |
ItemPrediction[] |
VectorSimilarityAlgorithm.getRecommendationsByType(int activeUser,
int n,
int type)
Not yet complete |
ItemPrediction[] |
VectorSimilarityAlgorithm.getRecommendations(int activeUser,
int n)
Not yet complete |
ItemPrediction |
VectorSimilarityAlgorithm.predictRating(int activeUser,
int item)
Predict rating for activeUser' item. |
long |
VectorSimilarityAlgorithm.getAveragePredictionTime()
Return average prediction time. |
void |
VectorSimilarityAlgorithm.updateUser(int userID)
Not yet complete. |
ItemPrediction[] |
UserItemAlgorithm.getRecommendations(int activeUser,
int n)
Not yet complete. |
ItemPrediction[] |
UserItemAlgorithm.getRecommendationsByType(int activeUser,
int n,
int type)
Not yet complete |
ItemPrediction |
UserItemAlgorithm.predictRating(int activeUser,
int item)
Predicts the rating for the given user and item. |
long |
UserItemAlgorithm.getAveragePredictionTime()
Return average prediction time. |
void |
UserItemAlgorithm.updateUser(int userID)
Not yet complete. |
ItemPrediction[] |
SimpleDistributionAlgorithm.getRecommendationsByType(int activeUser,
int n,
int type)
Return top n recommendations of specific type. |
ItemPrediction[] |
SimpleDistributionAlgorithm.getRecommendations(int activeUser,
int n)
Return top n recommendations. |
ItemPrediction |
SimpleDistributionAlgorithm.predictRating(int activeUser,
int item)
Predicts the rating for the given user and item. |
long |
SimpleDistributionAlgorithm.getAveragePredictionTime()
Return average prediction time. |
void |
SimpleDistributionAlgorithm.updateUser(int userID)
Haven't been implemented. |
ItemPrediction[] |
PopAlgorithm.getRecommendationsByType(int activeUser,
int n,
int type)
Yun Wang Return top n recommendations of specific type. |
ItemPrediction[] |
PopAlgorithm.getRecommendations(int activeUser,
int n)
Returns top n recommendations. |
ItemPrediction |
PopAlgorithm.predictRating(int activeUser,
int item)
Predicts the rating for the given user and item. |
void |
PopAlgorithm.updateUser(int userID)
Not yet implemented. |
long |
PopAlgorithm.getAveragePredictionTime()
Return average prediction time. |
ItemPrediction[] |
PersonalityAlgorithm.getRecommendationsByType(int activeUser,
int n,
int type)
Not yet complete |
ItemPrediction[] |
PersonalityAlgorithm.getRecommendations(int activeUser,
int n)
Not yet implemented. |
ItemPrediction |
PersonalityAlgorithm.predictRating(int activeUser,
int item)
predicts the rating for the given user and item |
void |
PersonalityAlgorithm.updateUser(int userID)
Not yet implemented. |
ItemPrediction[] |
ItemItemAlgorithm.getRecommendationsByType(int activeUser,
int n,
int type)
Yun Wang Return top n recommendations of specific type. |
ItemPrediction[] |
ItemItemAlgorithm.getRecommendations(int activeUser,
int n)
Get top n item recommendations for the activeUser. |
ItemPrediction |
ItemItemAlgorithm.predictRating(int activeUser,
int item)
predicts the rating for the given user and item |
void |
ItemItemAlgorithm.updateUser(int userID)
Currently does not need updateUser |
long |
ItemItemAlgorithm.getAveragePredictionTime()
Return average prediction time. |
ItemPrediction |
HortingAlgorithm.predictRating(int userID,
int itemID)
Get predicted rating for a user's item |
long |
HortingAlgorithm.getAveragePredictionTime()
Get average prediction time. |
ItemPrediction[] |
HortingAlgorithm.getRecommendationsByType(int activeUser,
int n,
int type)
Yun Wang Return top n recommendations of specific type. |
ItemPrediction[] |
HortingAlgorithm.getRecommendations(int activeUser,
int n)
Returns top n recommendations. |
void |
HortingAlgorithm.updateUser(int userID)
When new user comes in, update the neighborLists and maxUser and HortingArray. |
ItemPrediction[] |
DumbPopAlgorithm.getRecommendationsByType(int activeUser,
int n,
int type)
Yun Wang Return top n recommendations of specific type. |
ItemPrediction[] |
DumbPopAlgorithm.getRecommendations(int activeUser,
int n)
Returns top n recommendations. |
ItemPrediction |
DumbPopAlgorithm.predictRating(int activeUser,
int item)
|
void |
DumbPopAlgorithm.updateUser(int userID)
Requires no action. |
long |
DumbPopAlgorithm.getAveragePredictionTime()
Return average prediction time. |
ItemPrediction |
ClusteringAlgorithm.predictRating(int userID,
int itemID)
Asks the prediction engine to return a prediction for how a particular user will rate a particular item. |
long |
ClusteringAlgorithm.getAveragePredictionTime()
Return average prediction time |
ItemPrediction[] |
ClusteringAlgorithm.getRecommendations(int activeUser,
int n)
Not yet implemented. |
void |
ClusteringAlgorithm.updateUser(int userID)
Not yet implemented. |
ItemPrediction[] |
ClusteringAlgorithm.getRecommendationsByType(int activeUser,
int n,
int type)
Haven't been implemented yet. |
Constructors in org.recommender.algorithms.Experimental that throw CFNotImplementedException | |
BayesNetAlgorithm(DataManager mgr)
Get algorithm properties and initialize a BayesNet object. |
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CFEngine API | ||||||||||
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