Accuracy and diversity have long been considered to be two conflicting goals
for recommendations. We point out, however, that as the diversity is typically
measured by certain pre-selected item attributes, e.g., category as the most
popularly employed one, improved diversity can be achieved without sacrificing
recommendation accuracy, as long as the diversification respects the user's
preference about the pre-selected attributes. This calls for a fine-grained
understanding of a user's preferences over items, where one needs to recognize
the user's choice is driven by the quality of the item itself, or the
pre-selected attributes of the item. In this work, we focus on diversity
defined on item categories. We propose a general diversification framework
agnostic to the choice of recommendation algorithms. Our solution disentangles
the learnt user representation in the recommendation module into
category-independent and category-dependent components to differentiate a
user's preference over items from two orthogonal perspectives. Experimental
results on three benchmark datasets and online A/B test demonstrate the
effectiveness of our solution in improving both recommendation accuracy and
diversity. In-depth analysis suggests that the improvement is due to our
improved modeling of users' categorical preferences and refined ranking within
item categories.

本文提出了一个通用的推荐多样性框架，能在不牺牲精度的前提下实现商品类别的多样性，需要细致理解用户对商品的品质或预选属性的偏好，使用户表示与商品类别无关，通过改进模型能够在商品类别内改进用户分类喜好的建模与商品排序，通过实验证明了该模型对精度和多样性的提升。

多样化推荐的分离表示

Disentangled Representation for Diversified Recommendations

Recommender systems are personalized: we expect the results given to a
particular user to reflect that user's preferences. Some researchers have
studied the notion of calibration, how well recommendations match users' stated
preferences, and bias disparity the extent to which mis-calibration affects
different user groups. In this paper, we examine bias disparity over a range of
different algorithms and for different item categories and demonstrate
significant differences between model-based and memory-based algorithms.

本文研究了推荐系统中的个性化、校准、偏差差异等主题，检验了不同算法和物品类别之间的显著差异。