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Rating prediction is an important application, and a popular research topicin collaborative filtering. However, both the validity of learning algorithms,and the validity of standard testing procedures rest on the assumption thatmissing ratings are missing at random (MAR). In this paper we present theresults of a user study in which we collect a random sample of ratings fromcurrent users of an online radio service. An analysis of the rating datacollected in the study shows that the sample of random ratings has markedlydifferent properties than ratings of user-selected songs. When asked to reporton their own rating behaviour, a large number of users indicate they believetheir opinion of a song does affect whether they choose to rate that song, aviolation of the MAR condition. Finally, we present experimental resultsshowing that incorporating an explicit model of the missing data mechanism canlead to significant improvements in prediction performance on the random sampleof ratings.

Collaborative Filtering and the Missing at Random Assumption