Ciro Greco


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“Does it come in black?” CLIP-like models are zero-shot recommenders
Patrick John Chia | Jacopo Tagliabue | Federico Bianchi | Ciro Greco | Diogo Goncalves
Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)

Product discovery is a crucial component for online shopping. However, item-to-item recommendations today do not allow users to explore changes along selected dimensions: given a query item, can a model suggest something similar but in a different color? We consider item recommendations of the comparative nature (e.g. “something darker”) and show how CLIP-based models can support this use case in a zero-shot manner. Leveraging a large model built for fashion, we introduce GradREC and its industry potential, and offer a first rounded assessment of its strength and weaknesses.


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Language in a (Search) Box: Grounding Language Learning in Real-World Human-Machine Interaction
Federico Bianchi | Ciro Greco | Jacopo Tagliabue
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

We investigate grounded language learning through real-world data, by modelling a teacher-learner dynamics through the natural interactions occurring between users and search engines; in particular, we explore the emergence of semantic generalization from unsupervised dense representations outside of synthetic environments. A grounding domain, a denotation function and a composition function are learned from user data only. We show how the resulting semantics for noun phrases exhibits compositional properties while being fully learnable without any explicit labelling. We benchmark our grounded semantics on compositionality and zero-shot inference tasks, and we show that it provides better results and better generalizations than SOTA non-grounded models, such as word2vec and BERT.