Evaluation class for item recommendation
More...
|
| static IList< int > | Candidates (IList< int > candidate_items, CandidateItems candidate_item_mode, IPosOnlyFeedback test, IPosOnlyFeedback training) |
| |
| static double | ComputeFit (this ItemRecommender recommender, IList< int > test_users=null, IList< int > candidate_items=null, CandidateItems candidate_item_mode=CandidateItems.OVERLAP) |
| | Computes the AUC fit of a recommender on the training data More...
|
| |
| static ItemRecommendationEvaluationResults | Evaluate (this IRecommender recommender, IPosOnlyFeedback test, IPosOnlyFeedback training, IList< int > test_users=null, IList< int > candidate_items=null, CandidateItems candidate_item_mode=CandidateItems.OVERLAP, RepeatedEvents repeated_events=RepeatedEvents.No, int n=-1) |
| | Evaluation for rankings of items More...
|
| |
|
| static ICollection< string > | Measures [get] |
| | the evaluation measures for item prediction offered by the class More...
|
| |
Evaluation class for item recommendation
- Parameters
-
| candidate_items | a list of integers with all candidate items |
| candidate_item_mode | the mode used to determine the candidate items |
| test | test cases |
| training | training data |
| static double ComputeFit |
( |
this ItemRecommender |
recommender, |
|
|
IList< int > |
test_users = null, |
|
|
IList< int > |
candidate_items = null, |
|
|
CandidateItems |
candidate_item_mode = CandidateItems.OVERLAP |
|
) |
| |
|
inlinestatic |
Computes the AUC fit of a recommender on the training data
- Returns
- the AUC on the training data
- Parameters
-
| recommender | the item recommender to evaluate |
| test_users | a list of integers with all test users; if null, use all users in the test cases |
| candidate_items | a list of integers with all candidate items |
| candidate_item_mode | the mode used to determine the candidate items |
Evaluation for rankings of items
User-item combinations that appear in both sets are ignored for the test set, and thus in the evaluation, except the boolean argument repeated_events is set.
The evaluation measures are listed in the Measures property. Additionally, 'num_users' and 'num_items' report the number of users that were used to compute the results and the number of items that were taken into account.
Literature:
-
C. Manning, P. Raghavan, H. Schütze: Introduction to Information Retrieval, Cambridge University Press, 2008
On multi-core/multi-processor systems, the routine tries to use as many cores as possible, which should to an almost linear speed-up.
- Parameters
-
| recommender | item recommender |
| test | test cases |
| training | training data |
| test_users | a list of integers with all test users; if null, use all users in the test cases |
| candidate_items | a list of integers with all candidate items |
| candidate_item_mode | the mode used to determine the candidate items |
| repeated_events | allow repeated events in the evaluation (i.e. items accessed by a user before may be in the recommended list) |
| n | length of the item list to evaluate – if set to -1 (default), use the complete list, otherwise compute evaluation measures on the top n items |
- Returns
- a dictionary containing the evaluation results (default is false)
| ICollection<string> Measures |
|
staticget |
the evaluation measures for item prediction offered by the class
The evaluation measures currently are:
-
AUCarea under the ROC curve
-
prec@5precision at 5
-
prec@10precision at 10
-
MAPmean average precision
-
recall@5recall at 5
-
recall@10recall at 10
-
NDCGnormalizad discounted cumulative gain
-
MRRmean reciprocal rank
An item recommender is better than another according to one of those measures its score is higher.
The documentation for this class was generated from the following file:
