  Recommender systems have made significant strides in various industries,
primarily driven by extensive efforts to enhance recommendation accuracy.
However, this pursuit of accuracy has inadvertently given rise to echo
chamber/filter bubble effects. Especially in industry, it could impair user's
experiences and prevent user from accessing a wider range of items. One of the
solutions is to take diversity into account. However, most of existing works
focus on user's explicit preferences, while rarely exploring user's
non-interaction preferences. These neglected non-interaction preferences are
especially important for broadening user's interests in alleviating echo
chamber/filter bubble effects.Therefore, in this paper, we first define
diversity as two distinct definitions, i.e., user-explicit diversity
(U-diversity) and user-item non-interaction diversity (N-diversity) based on
user historical behaviors. Then, we propose a succinct and effective method,
named as Controllable Category Diversity Framework (CCDF) to achieve both high
U-diversity and N-diversity simultaneously.Specifically, CCDF consists of two
stages, User-Category Matching and Constrained Item Matching. The User-Category
Matching utilizes the DeepU2C model and a combined loss to capture user's
preferences in categories, and then selects the top-$K$ categories with a
controllable parameter $K$.These top-$K$ categories will be used as trigger
information in Constrained Item Matching. Offline experimental results show
that our proposed DeepU2C outperforms state-of-the-art diversity-oriented
methods, especially on N-diversity task. The whole framework is validated in a
real-world production environment by conducting online A/B testing.
