  As the final stage of the multi-stage recommender system (MRS), reranking
directly affects users' experience and satisfaction, thus playing a critical
role in MRS. Despite the improvement achieved in the existing work, three
issues are yet to be solved. First, users' historical behaviors contain rich
preference information, such as users' long and short-term interests, but are
not fully exploited in reranking. Previous work typically treats items in
history equally important, neglecting the dynamic interaction between the
history and candidate items. Second, existing reranking models focus on
learning interactions at the item level while ignoring the fine-grained
feature-level interactions. Lastly, estimating the reranking score on the
ordered initial list before reranking may lead to the early scoring problem,
thereby yielding suboptimal reranking performance. To address the above issues,
we propose a framework named Multi-level Interaction Reranking (MIR). MIR
combines low-level cross-item interaction and high-level set-to-list
interaction, where we view the candidate items to be reranked as a set and the
users' behavior history in chronological order as a list. We design a novel
SLAttention structure for modeling the set-to-list interactions with
personalized long-short term interests. Moreover, feature-level interactions
are incorporated to capture the fine-grained influence among items. We design
MIR in such a way that any permutation of the input items would not change the
output ranking, and we theoretically prove it. Extensive experiments on three
public and proprietary datasets show that MIR significantly outperforms the
state-of-the-art models using various ranking and utility metrics.
