2010 TrainingandTestingofRecommender

• (Steck, 2010) ⇒ Harald Steck. (2010). “Training and Testing of Recommender Systems on Data Missing Not at Random.” In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2010). doi:10.1145/1835804.1835895

Subject Headings: Missing-not-at-Random (MNAR) Data.

Notes

• Categories and Subject Descriptors: H.2.8 Database Management: Database Applications – Data Mining.
• General Terms: Algorithms

Cited By

• http://scholar.google.com/scholar?q=%22Training+and+testing+of+recommender+systems+on+data+missing+not+at+random%22+2010
• http://portal.acm.org/citation.cfm?id=1835895&preflayout=flat#citedby

Quotes

Author Keywords

Recommender Systems, MNAR Data.

Abstract

Users typically rate only a small fraction of all available items. [[2010_TrainingandTestingofRecommender

|We]] show that the absence of ratings carries useful information for improving the top-k hit rate concerning all items, a natural accuracy measure for recommendations. As to test recommender systems, we present two performance measures that can be estimated, under mild assumptions, without bias from data even when ratings are missing not at random (MNAR). As to achieve optimal test results, we present appropriate surrogate objective functions for efficient training on MNAR data. Their main property is to account for all ratings -- whether observed or missing in the data. Concerning the top-k hit rate on test data, our experiments indicate dramatic improvements over even sophisticated methods that are optimized on observed ratings only.

References

,

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 TrainingandTestingofRecommender	Harald Steck			Training and Testing of Recommender Systems on Data Missing Not at Random		KDD-2010 Proceedings		10.1145/1835804.1835895		2010