|
Abstract:
|
This talk investigates informative missingness in the frame work of recommender systems. For example, in 2009, Netflix ran a $1M prize competition to improve their algorithm to recommend movies to their viewers. In this setting, we can imagine a potential rating for every object-user pair. For Netflix, the object would be the movie. However, the vast majority of these pairs are missing. The goal of a recommender system is to predict these missing ratings in order to recommend an object that the user is likely to rate highly. A typically overlooked piece is that the combinations are not missing at random. A relationship between user ratings and their viewing history is expected, as human nature dictates the user would seek out and watch movies that they anticipate enjoying. We model this informative missingness, and place the recommender system in a shared-variable regression framework. We show that taking this additional information into account can aid in prediction quality.
|
