  The study of user interest models has received a great deal of attention in
click through rate (CTR) prediction recently. These models aim at capturing
user interest from different perspectives, including user interest evolution,
session interest, multiple interests, etc. In this paper, we focus on a new
type of user interest, i.e., user retargeting interest. User retargeting
interest is defined as user's click interest on target items the same as or
similar to historical click items. We propose a novel soft retargeting network
(SRN) to model this specific interest. Specifically, we first calculate the
similarity between target item and each historical item with the help of graph
embedding. Then we learn to aggregate the similarity weights to measure the
extent of user's click interest on target item. Furthermore, we model the
evolution of user retargeting interest. Experimental results on public datasets
and industrial dataset demonstrate that our model achieves significant
improvements over state-of-the-art models.
