Collaborative Filtering and the Missing at Random Assumption

Understand

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.