  Traditionally, heuristic methods are used to generate candidates for large
scale recommender systems. Model-based candidate generation promises multiple
potential advantages, primarily that we can explicitly optimize the same
objective as the downstream ranking model. However, large scale model-based
candidate generation approaches suffer from dataset bias problems caused by the
infeasibility of obtaining representative data on very irrelevant candidates.
Popular techniques to correct dataset bias, such as inverse propensity scoring,
do not work well in the context of candidate generation. We first explore the
dynamics of the dataset bias problem and then demonstrate how to use random
sampling techniques to mitigate it. Finally, in a novel application of
fine-tuning, we show performance gains when applying our candidate generation
system to Twitter's home timeline.
