  Big models are widely used by online recommender systems to boost
recommendation performance. They are usually learned on historical user
behavior data to infer user interest and predict future user behaviors (e.g.,
clicks). In fact, the behaviors of heavy users with more historical behaviors
can usually provide richer clues than cold users in interest modeling and
future behavior prediction. Big models may favor heavy users by learning more
from their behavior patterns and bring unfairness to cold users. In this paper,
we study whether big recommendation models are fair to cold users. We
empirically demonstrate that optimizing the overall performance of big
recommendation models may lead to unfairness to cold users in terms of
performance degradation. To solve this problem, we propose a BigFair method
based on self-distillation, which uses the model predictions on original user
data as a teacher to regularize predictions on augmented data with randomly
dropped user behaviors, which can encourage the model to fairly capture
interest distributions of heavy and cold users. Experiments on two datasets
show that BigFair can effectively improve the performance fairness of big
recommendation models on cold users without harming the performance on heavy
users.
