  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.
