  In a collaborative-filtering recommendation scenario, biases in the data will
likely propagate in the learned recommendations. In this paper we focus on the
so-called mainstream bias: the tendency of a recommender system to provide
better recommendations to users who have a mainstream taste, as opposed to
non-mainstream users. We propose NAECF, a conceptually simple but effective
idea to address this bias. The idea consists of adding an autoencoder (AE)
layer when learning user and item representations with text-based Convolutional
Neural Networks. The AEs, one for the users and one for the items, serve as
adversaries to the process of minimizing the rating prediction error when
learning how to recommend. They enforce that the specific unique properties of
all users and items are sufficiently well incorporated and preserved in the
learned representations. These representations, extracted as the bottlenecks of
the corresponding AEs, are expected to be less biased towards mainstream users,
and to provide more balanced recommendation utility across all users. Our
experimental results confirm these expectations, significantly improving the
recommendations for non-mainstream users while maintaining the recommendation
quality for mainstream users. Our results emphasize the importance of deploying
extensive content-based features, such as online reviews, in order to better
represent users and items to maximize the de-biasing effect.
