  Recommender systems usually amplify the biases in the data. The model learned
from historical interactions with imbalanced item distribution will amplify the
imbalance by over-recommending items from the major groups. Addressing this
issue is essential for a healthy ecosystem of recommendation in the long run.
Existing works apply bias control to the ranking targets (e.g., calibration,
fairness, and diversity), but ignore the true reason for bias amplification and
trade-off the recommendation accuracy.
  In this work, we scrutinize the cause-effect factors for bias amplification,
identifying the main reason lies in the confounder effect of imbalanced item
distribution on user representation and prediction score. The existence of such
confounder pushes us to go beyond merely modeling the conditional probability
and embrace the causal modeling for recommendation. Towards this end, we
propose a Deconfounded Recommender System (DecRS), which models the causal
effect of user representation on the prediction score. The key to eliminating
the impact of the confounder lies in backdoor adjustment, which is however
difficult to do due to the infinite sample space of the confounder. For this
challenge, we contribute an approximation operator for backdoor adjustment
which can be easily plugged into most recommender models. Lastly, we devise an
inference strategy to dynamically regulate backdoor adjustment according to
user status. We instantiate DecRS on two representative models FM and NFM, and
conduct extensive experiments over two benchmarks to validate the superiority
of our proposed DecRS.
