Language models that utilize extensive self-supervised pre-training from
unlabeled text, have recently shown to significantly advance the
state-of-the-art performance in a variety of language understanding tasks.
However, it is yet unclear if and how these recent models can be harnessed for
conducting text-based recommendations. In this work, we introduce RecoBERT, a
BERT-based approach for learning catalog-specialized language models for
text-based item recommendations. We suggest novel training and inference
procedures for scoring similarities between pairs of items, that don't require
item similarity labels. Both the training and the inference techniques were
designed to utilize the unlabeled structure of textual catalogs, and minimize
the discrepancy between them. By incorporating four scores during inference,
RecoBERT can infer text-based item-to-item similarities more accurately than
other techniques. In addition, we introduce a new language understanding task
for wine recommendations using similarities based on professional wine reviews.
As an additional contribution, we publish annotated recommendations dataset
crafted by human wine experts. Finally, we evaluate RecoBERT and compare it to
various state-of-the-art NLP models on wine and fashion recommendations tasks.

本研究提出了 RecoBERT，一种基于 BERT 的方法，用于学习目录专用的语言模型以实现基于文本的商品推荐，并通过独创的训练和推理过程，在不需要商品相似性标签的情况下评分，以更准确地推断商品之间的文本相似性，并将其应用到葡萄酒和时尚商品推荐任务中。