Items Class Reference

Evaluation class for item recommendation. More...

List of all members.

Static Public Member Functions
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.
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, bool repeated_events=false)
	Evaluation for rankings of items.
Properties
static ICollection< string >	Measures [get]
	the evaluation measures for item prediction offered by the class

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Detailed Description

Evaluation class for item recommendation.

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Member Function Documentation

static double ComputeFit	(	this ItemRecommender	recommender,
		IList< int >	test_users = null,
		IList< int >	candidate_items = null,
		CandidateItems	candidate_item_mode = CandidateItems.OVERLAP
	)			[inline, static]

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

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,
		bool	repeated_events = false
	)			[inline, static]

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)

Returns:

a dictionary containing the evaluation results (default is false)

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Property Documentation

ICollection<string> Measures [static, get]

the evaluation measures for item prediction offered by the class

The evaluation measures currently are:

• AUCarea under the ROC curve
• precprecision at 5
• precprecision at 10
• MAPmean average precision
• recallrecall at 5
• recallrecall 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.

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The documentation for this class was generated from the following file:

• Eval/Items.cs