  The goal of recommender systems is to provide ordered item lists to users
that best match their interests. As a critical task in the recommendation
pipeline, re-ranking has received increasing attention in recent years. In
contrast to conventional ranking models that score each item individually,
re-ranking aims to explicitly model the mutual influences among items to
further refine the ordering of items given an initial ranking list. In this
paper, we present a personalized re-ranking model (dubbed PEAR) based on
contextualized transformer. PEAR makes several major improvements over the
existing methods. Specifically, PEAR not only captures feature-level and
item-level interactions, but also models item contexts from both the initial
ranking list and the historical clicked item list. In addition to item-level
ranking score prediction, we also augment the training of PEAR with a
list-level classification task to assess users' satisfaction on the whole
ranking list. Experimental results on both public and production datasets have
shown the superior effectiveness of PEAR compared to the previous re-ranking
models.
