  Recommender Systems (RSs) provide personalized recommendation service based
on user interest, which are widely used in various platforms. However, there
are lots of users with sparse interest due to lacking consumption behaviors,
which leads to poor recommendation results for them. This problem is widespread
in large-scale RSs and is particularly difficult to address. To solve this
problem, we propose a novel solution named User Interest Enhancement (UIE)
which enhances user interest including user profile and user history behavior
sequences using the enhancement vectors and personalized enhancement vector
generated based on stream clustering and memory networks from different
perspectives. UIE not only remarkably improves model performance on the users
with sparse interest but also significantly enhance model performance on other
users. UIE is an end-to-end solution which is easy to be implemented based on
ranking model. Moreover, we expand our solution and apply similar methods to
long-tail items, which also achieves excellent improvement. Furthermore, we
conduct extensive offline and online experiments in a large-scale industrial
RS. The results demonstrate that our model outperforms other models remarkably,
especially for the users with sparse interest. Until now, UIE has been fully
deployed in multiple large-scale RSs and achieved remarkable improvements.
