Recommender systems have been widely used in e-commerce, and re-ranking
models are playing an increasingly significant role in the domain, which
leverages the inter-item influence and determines the final recommendation
lists. Online learning methods keep updating a deployed model with the latest
available samples to capture the shifting of the underlying data distribution
in e-commerce. However, they depend on the availability of real user feedback,
which may be delayed by hours or even days, such as item purchases, leading to
a lag in model enhancement. In this paper, we propose a novel extension of
online learning methods for re-ranking modeling, which we term LAST, an acronym
for Learning At Serving Time. It circumvents the requirement of user feedback
by using a surrogate model to provide the instructional signal needed to steer
model improvement. Upon receiving an online request, LAST finds and applies a
model modification on the fly before generating a recommendation result for the
request. The modification is request-specific and transient. It means the
modification is tailored to and only to the current request to capture the
specific context of the request. After a request, the modification is
discarded, which helps to prevent error propagation and stabilizes the online
learning procedure since the predictions of the surrogate model may be
inaccurate. Most importantly, as a complement to feedback-based online learning
methods, LAST can be seamlessly integrated into existing online learning
systems to create a more adaptive and responsive recommendation experience.
Comprehensive experiments, both offline and online, affirm that LAST
outperforms state-of-the-art re-ranking models.

在线学习方法的扩展 LAST 能够无缝集成到现有的在线学习系统中，对电子商务中的推荐系统进行自适应和响应式的建模和推荐，以提供更好的推荐体验。

无需用户反馈的电商学习重排模型的即时学习

Do Not Wait: Learning Re-Ranking Model Without User Feedback At Serving  Time in E-Commerce

The BEIR dataset is a large, heterogeneous benchmark for Information
Retrieval (IR) in zero-shot settings, garnering considerable attention within
the research community. However, BEIR and analogous datasets are predominantly
restricted to the English language. Our objective is to establish extensive
large-scale resources for IR in the Polish language, thereby advancing the
research in this NLP area. In this work, inspired by mMARCO and Mr.~TyDi
datasets, we translated all accessible open IR datasets into Polish, and we
introduced the BEIR-PL benchmark -- a new benchmark which comprises 13
datasets, facilitating further development, training and evaluation of modern
Polish language models for IR tasks. We executed an evaluation and comparison
of numerous IR models on the newly introduced BEIR-PL benchmark. Furthermore,
we publish pre-trained open IR models for Polish language,d marking a
pioneering development in this field. Additionally, the evaluation revealed
that BM25 achieved significantly lower scores for Polish than for English,
which can be attributed to high inflection and intricate morphological
structure of the Polish language. Finally, we trained various re-ranking models
to enhance the BM25 retrieval, and we compared their performance to identify
their unique characteristic features. To ensure accurate model comparisons, it
is necessary to scrutinise individual results rather than to average across the
entire benchmark. Thus, we thoroughly analysed the outcomes of IR models in
relation to each individual data subset encompassed by the BEIR benchmark. The
benchmark data is available at URL {\bf this https URL}.

本文旨在建立广泛的大规模波兰语信息检索资源，翻译所有可获得的开放式信息检索数据集并引入 BEIR-PL 基准测试，对其中的数据集进行评估和比较，以进行先进波兰语检索模型的进一步开发、训练和评估，并评估各种 IR 模型的性能，并训练了各种重新排序模型来增强 BM25 检索。