Sparse offline recommender rankings correlate only weakly—and sometimes negatively—with dense ground-truth rankings, and no evaluation design is uniformly best.
Collaborative Filtering and the Missing at Random Assumption
1 Pith paper cite this work, alongside 187 external citations. Polarity classification is still indexing.
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abstract
Rating prediction is an important application, and a popular research topic in collaborative filtering. However, both the validity of learning algorithms, and the validity of standard testing procedures rest on the assumption that missing ratings are missing at random (MAR). In this paper we present the results of a user study in which we collect a random sample of ratings from current users of an online radio service. An analysis of the rating data collected in the study shows that the sample of random ratings has markedly different properties than ratings of user-selected songs. When asked to report on their own rating behaviour, a large number of users indicate they believe their opinion of a song does affect whether they choose to rate that song, a violation of the MAR condition. Finally, we present experimental results showing that incorporating an explicit model of the missing data mechanism can lead to significant improvements in prediction performance on the random sample of ratings.
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On the Convergent Validity of Offline Evaluation Designs for Recommender Systems
Sparse offline recommender rankings correlate only weakly—and sometimes negatively—with dense ground-truth rankings, and no evaluation design is uniformly best.