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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T RecoBERT: A Catalog Language Model for Text-Based Recommendations
%A Malkiel, Itzik
%A Barkan, Oren
%A Caciularu, Avi
%A Razin, Noam
%A Katz, Ori
%A Koenigstein, Noam
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F malkiel-etal-2020-recobert
%X 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.
%R 10.18653/v1/2020.findings-emnlp.154
%U https://aclanthology.org/2020.findings-emnlp.154
%U https://doi.org/10.18653/v1/2020.findings-emnlp.154
%P 1704-1714

Markdown (Informal)

[RecoBERT: A Catalog Language Model for Text-Based Recommendations](https://aclanthology.org/2020.findings-emnlp.154) (Malkiel et al., Findings 2020)

• RecoBERT: A Catalog Language Model for Text-Based Recommendations (Malkiel et al., Findings 2020)

ACL

• Itzik Malkiel, Oren Barkan, Avi Caciularu, Noam Razin, Ori Katz, and Noam Koenigstein. 2020. RecoBERT: A Catalog Language Model for Text-Based Recommendations. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1704–1714, Online. Association for Computational Linguistics.