# Slate-Aware Ranking for Recommendation

## TL;DR

This paper introduces Slate-Aware Ranking (SAR), a novel method that improves candidate set quality in slate recommender systems by implicitly modeling item relations during the ranking stage, enhancing overall relevance and diversity.

## Contribution

The paper proposes a new slate-aware ranking approach that enhances candidate set quality by modeling item relations during ranking, improving subsequent re-ranking effectiveness.

## Key findings

- SAR improves recommendation relevance and diversity.
- Experimental results show SAR outperforms baseline methods.
- Online A/B tests confirm SAR's effectiveness in real systems.

## Abstract

We see widespread adoption of slate recommender systems, where an ordered item list is fed to the user based on the user interests and items' content. For each recommendation, the user can select one or several items from the list for further interaction. In this setting, the significant impact on user behaviors from the mutual influence among the items is well understood. The existing methods add another step of slate re-ranking after the ranking stage of recommender systems, which considers the mutual influence among recommended items to re-rank and generate the recommendation results so as to maximize the expected overall utility. However, to model the complex interaction of multiple recommended items, the re-ranking stage usually can just handle dozens of candidates because of the constraint of limited hardware resource and system latency. Therefore, the ranking stage is still essential for most applications to provide high-quality candidate set for the re-ranking stage. In this paper, we propose a solution named Slate-Aware ranking (SAR) for the ranking stage. By implicitly considering the relations among the slate items, it significantly enhances the quality of the re-ranking stage's candidate set and boosts the relevance and diversity of the overall recommender systems. Both experiments with the public datasets and internal online A/B testing are conducted to verify its effectiveness.