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public interface RelevantItemsDataSplitter
Implementations of this interface determine the items that are considered relevant,
and splits data into a training and test subset, for purposes of precision/recall
tests as implemented by implementations of RecommenderIRStatsEvaluator
.
Method Summary | |
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FastIDSet |
getRelevantItemsIDs(long userID,
int at,
double relevanceThreshold,
DataModel dataModel)
During testing, relevant items are removed from a particular users' preferences, and a model is build using this user's other preferences and all other users. |
void |
processOtherUser(long userID,
FastIDSet relevantItemIDs,
FastByIDMap<PreferenceArray> trainingUsers,
long otherUserID,
DataModel dataModel)
Adds a single user and all their preferences to the training model. |
Method Detail |
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FastIDSet getRelevantItemsIDs(long userID, int at, double relevanceThreshold, DataModel dataModel) throws TasteException
at
- Maximum number of items to be removedrelevanceThreshold
- Minimum strength of preference for an item to be considered
relevant
TasteException
void processOtherUser(long userID, FastIDSet relevantItemIDs, FastByIDMap<PreferenceArray> trainingUsers, long otherUserID, DataModel dataModel) throws TasteException
userID
- ID of user whose preferences we are trying to predictrelevantItemIDs
- IDs of items considered relevant to that usertrainingUsers
- the database of training preferences to which we will
append the ones for otherUserID.otherUserID
- for whom we are adding preferences to the training model
TasteException
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