  Recommender system usually faces popularity bias issues: from the data
perspective, items exhibit uneven (long-tail) distribution on the interaction
frequency; from the method perspective, collaborative filtering methods are
prone to amplify the bias by over-recommending popular items. It is undoubtedly
critical to consider popularity bias in recommender systems, and existing work
mainly eliminates the bias effect. However, we argue that not all biases in the
data are bad -- some items demonstrate higher popularity because of their
better intrinsic quality. Blindly pursuing unbiased learning may remove the
beneficial patterns in the data, degrading the recommendation accuracy and user
satisfaction.
  This work studies an unexplored problem in recommendation -- how to leverage
popularity bias to improve the recommendation accuracy. The key lies in two
aspects: how to remove the bad impact of popularity bias during training, and
how to inject the desired popularity bias in the inference stage that generates
top-K recommendations. This questions the causal mechanism of the
recommendation generation process. Along this line, we find that item
popularity plays the role of confounder between the exposed items and the
observed interactions, causing the bad effect of bias amplification. To achieve
our goal, we propose a new training and inference paradigm for recommendation
named Popularity-bias Deconfounding and Adjusting (PDA). It removes the
confounding popularity bias in model training and adjusts the recommendation
score with desired popularity bias via causal intervention. We demonstrate the
new paradigm on latent factor model and perform extensive experiments on three
real-world datasets. Empirical studies validate that the deconfounded training
is helpful to discover user real interests and the inference adjustment with
popularity bias could further improve the recommendation accuracy.
