  With the rapid development of E-commerce and the increase in the quantity of
items, users are presented with more items hence their interests broaden. It is
increasingly difficult to model user intentions with traditional methods, which
model the user's preference for an item by combining a single user vector and
an item vector. Recently, some methods are proposed to generate multiple user
interest vectors and achieve better performance compared to traditional
methods. However, empirical studies demonstrate that vectors generated from
these multi-interests methods are sometimes homogeneous, which may lead to
sub-optimal performance. In this paper, we propose a novel method of Diversity
Regularized Interests Modeling (DRIM) for Recommender Systems. We apply a
capsule network in a multi-interest extractor to generate multiple user
interest vectors. Each interest of the user should have a certain degree of
distinction, thus we introduce three strategies as the diversity regularized
separator to separate multiple user interest vectors. Experimental results on
public and industrial data sets demonstrate the ability of the model to capture
different interests of a user and the superior performance of the proposed
approach.
