Web applications where users are presented with a limited selection of items
have long employed ranking models to put the most relevant results first. Any
feedback received from users is typically assumed to reflect a relative
judgement on the utility of items, e.g. a user clicking on an item only implies
it is better than items not clicked in the same ranked list. Hence, the
objectives optimized in Learning-to-Rank (LTR) tend to be pairwise or listwise.
Yet, by only viewing feedback as relative, we neglect the user's absolute
feedback on the list's overall quality, e.g. when no items in the selection are
clicked. We thus reconsider the standard LTR paradigm and argue the benefits of
learning from this listwide signal. To this end, we propose the RankFormer as
an architecture that, with a Transformer at its core, can jointly optimize a
novel listwide assessment objective and a traditional listwise LTR objective.
We simulate implicit feedback on public datasets and observe that the
RankFormer succeeds in benefitting from listwide signals. Additionally, we
conduct experiments in e-commerce on Amazon Search data and find the RankFormer
to be superior to all baselines offline. An online experiment shows that
knowledge distillation can be used to find immediate practical use for the
RankFormer.

提出使用 RankFormer 架构来从用户的绝对反馈中学习列表的总体质量并优化排序，模拟的公共数据集与 Amazon 搜索数据集的离线实验结果表明 RankFormer 优于所有基线；通过知识蒸馏可立即在在线实验中使用