Constant item recommender for use as experimental baseline. Always predicts a score of zero
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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 More...
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Object | Clone () |
| create a shallow copy of the object More...
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override void | LoadModel (string filename) |
| Get the model parameters from a file More...
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override float | Predict (int user_id, int item_id) |
| Predict rating or score for a given user-item combination More...
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IList< Tuple< int, float > > | Recommend (int user_id, int n=-1, ICollection< int > ignore_items=null, ICollection< int > candidate_items=null) |
| Recommend items for a given user More...
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virtual System.Collections.Generic.IList< Tuple< int, float > > | Recommend (int user_id, int n=-1, System.Collections.Generic.ICollection< int > ignore_items=null, System.Collections.Generic.ICollection< int > candidate_items=null) |
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override void | SaveModel (string filename) |
| Save the model parameters to a file More...
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override string | ToString () |
| Return a string representation of the recommender More...
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override void | Train () |
| Learn the model parameters of the recommender from the training data More...
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Constant item recommender for use as experimental baseline. Always predicts a score of zero
This recommender can be used for debugging, e.g. to detect non-random orderings in item lists.
virtual bool CanPredict |
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int |
user_id, |
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int |
item_id |
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inlinevirtualinherited |
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.
- Parameters
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user_id | the user ID |
item_id | the item ID |
- Returns
- true if a useful prediction can be made, false otherwise
Implements IRecommender.
Reimplemented in ExternalItemRecommender, ExternalRatingPredictor, BiPolarSlopeOne, SlopeOne, Constant, GlobalAverage, UserAverage, ItemAverage, and Random.