CFEngine API

Uses of Class
org.recommender.algorithms.CFNotImplementedException

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.
 


CFEngine API

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