  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.
