  In this era of information explosion, a personalized recommendation system is
convenient for users to get information they are interested in. To deal with
billions of users and items, large-scale online recommendation services usually
consist of three stages: candidate generation, coarse-grained ranking, and
fine-grained ranking. The success of each stage depends on whether the model
accurately captures the interests of users, which are usually hidden in users'
behavior data. Previous research shows that users' interests are diverse, and
one vector is not sufficient to capture users' different preferences.
Therefore, many methods use multiple vectors to encode users' interests.
However, there are two unsolved problems: (1) The similarity of different
vectors in existing methods is too high, with too much redundant information.
Consequently, the interests of users are not fully represented. (2) Existing
methods model the long-term and short-term behaviors together, ignoring the
differences between them. This paper proposes a Hierarchical Multi-Interest
Co-Network (HCN) to capture users' diverse interests in the coarse-grained
ranking stage. Specifically, we design a hierarchical multi-interest extraction
layer to update users' diverse interest centers iteratively. The multiple
embedded vectors obtained in this way contain more information and represent
the interests of users better in various aspects. Furthermore, we develop a
Co-Interest Network to integrate users' long-term and short-term interests.
Experiments on several real-world datasets and one large-scale industrial
dataset show that HCN effectively outperforms the state-of-the-art methods. We
deploy HCN into a large-scale real world E-commerce system and achieve extra
2.5\% improvements on GMV (Gross Merchandise Value).
