  Recently, neural networks have been widely used in e-commerce recommender
systems, owing to the rapid development of deep learning. We formalize the
recommender system as a sequential recommendation problem, intending to predict
the next items that the user might be interacted with. Recent works usually
give an overall embedding from a user's behavior sequence. However, a unified
user embedding cannot reflect the user's multiple interests during a period. In
this paper, we propose a novel controllable multi-interest framework for the
sequential recommendation, called ComiRec. Our multi-interest module captures
multiple interests from user behavior sequences, which can be exploited for
retrieving candidate items from the large-scale item pool. These items are then
fed into an aggregation module to obtain the overall recommendation. The
aggregation module leverages a controllable factor to balance the
recommendation accuracy and diversity. We conduct experiments for the
sequential recommendation on two real-world datasets, Amazon and Taobao.
Experimental results demonstrate that our framework achieves significant
improvements over state-of-the-art models. Our framework has also been
successfully deployed on the offline Alibaba distributed cloud platform.
