Collaborative filtering (CF) based recommendations suffer from mainstream
bias -- where mainstream users are favored over niche users, leading to poor
recommendation quality for many long-tail users. In this paper, we identify two
root causes of this mainstream bias: (i) discrepancy modeling, whereby CF
algorithms focus on modeling mainstream users while neglecting niche users with
unique preferences; and (ii) unsynchronized learning, where niche users require
more training epochs than mainstream users to reach peak performance. Targeting
these causes, we propose a novel end-To-end Adaptive Local Learning (TALL)
framework to provide high-quality recommendations to both mainstream and niche
users. TALL uses a loss-driven Mixture-of-Experts module to adaptively ensemble
experts to provide customized local models for different users. Further, it
contains an adaptive weight module to synchronize the learning paces of
different users by dynamically adjusting weights in the loss. Extensive
experiments demonstrate the state-of-the-art performance of the proposed model.
Code and data are provided at
https://github.com/JP-25/end-To-end-Adaptive-Local-Leanring-TALL-

通过提出一种名为 TALL 的新型端到端自适应本地学习框架，研究通过缓解主流偏见来改善基于协同过滤的推荐算法，在提高主流和小众用户的推荐质量方面取得了最先进的性能。

通过端对端自适应局部学习对抗主流偏见

Countering Mainstream Bias via End-to-End Adaptive Local Learning

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.

介绍了一种名为 NAECF 的概念简单而有效的算法来解决协作过滤推荐中主流偏见的问题，该方法通过添加自编码器层来学习用户和物品特征，从而使得机器学习到的表示更加均衡，降低了针对主流用户的偏见。