Collaborative prediction and ranking with non-random missing data

A fundamental aspect of rating-based recommender systems is the observation process, the process by which users choose the items they rate. Nearly all research on collaborative filtering and recommender systems is founded on the assumption that missing ratings are missing at random. The statistical theory of missing data shows that incorrect assumptions about missing data can lead to biased parameter estimation and prediction. In a recent study, we demonstrated strong evidence for violations of the missing at random condition in a real recommender system. In this paper we present the first study of the effect of non-random missing data on collaborative ranking, and extend our previous results regarding the impact of non-random missing data on collaborative prediction. Categories and Subject Descriptors H.3.3 [Information Search and Retrieval]: Information filtering; G.3 [Probability and Statistics]: Multivariate statistics; I.2.6 [Learning]: Parameter learning General Terms Algori...

• Type: Conference
• Year: 2009
• Where: RECSYS
• Authors: Benjamin M. Marlin, Richard S. Zemel
• Keywords: Control Systems, Non-random Missing Data, Rating-based Recommender Systems, Recommender Systems, RECSYS 2009