Noam Koenigstein


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Self-Supervised Document Similarity Ranking via Contextualized Language Models and Hierarchical Inference
Dvir Ginzburg | Itzik Malkiel | Oren Barkan | Avi Caciularu | Noam Koenigstein
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021


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RecoBERT: A Catalog Language Model for Text-Based Recommendations
Itzik Malkiel | Oren Barkan | Avi Caciularu | Noam Razin | Ori Katz | Noam Koenigstein
Findings of the Association for Computational Linguistics: EMNLP 2020

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.

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Bayesian Hierarchical Words Representation Learning
Oren Barkan | Idan Rejwan | Avi Caciularu | Noam Koenigstein
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

This paper presents the Bayesian Hierarchical Words Representation (BHWR) learning algorithm. BHWR facilitates Variational Bayes word representation learning combined with semantic taxonomy modeling via hierarchical priors. By propagating relevant information between related words, BHWR utilizes the taxonomy to improve the quality of such representations. Evaluation of several linguistic datasets demonstrates the advantages of BHWR over suitable alternatives that facilitate Bayesian modeling with or without semantic priors. Finally, we further show that BHWR produces better representations for rare words.