  Rank models play a key role in industrial recommender systems, advertising,
and search engines. Existing works utilize semantic tags and user-item
interaction behaviors, e.g., clicks, views, etc., to predict the user interest
and the item hidden representation for estimating the user-item preference
score. However, these behavior-tag-based models encounter great challenges and
reduced effectiveness when user-item interaction activities are insufficient,
which we called "the long-tail ranking problem". Existing rank models ignore
this problem, but its common and important because any user or item can be
long-tailed once they are not consistently active for a short period. In this
paper, we propose a novel neighbor enhancement structure to help train the
representation of the target user or item. It takes advantage of similar
neighbors (static or dynamic similarity) with multi-level attention operations
balancing the weights of different neighbors. Experiments on the well-known
public dataset MovieLens 1M demonstrate the efficiency of the method over the
baseline behavior-tag-based model with an absolute CTR AUC gain of 0.0259 on
the long-tail user dataset.
