8 citations · 13 across the 5 of their papers we have counts for
8 papers
"Does it come in black?" CLIP-like models are zero-shot recommenders
Patrick John Chia, Jacopo Tagliabue, Federico Bianchi +2
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 q…
You Do Not Need a Bigger Boat: Recommendations at Reasonable Scale in a (Mostly) Serverless and Open Stack
Jacopo Tagliabue
We argue that immature data pipelines are preventing a large portion of industry practitioners from leveraging the latest research on recommender systems. We propose our template d…
"Are you sure?": Preliminary Insights from Scaling Product Comparisons to Multiple Shops
Patrick John Chia, Bingqing Yu, Jacopo Tagliabue
Large eCommerce players introduced comparison tables as a new type of recommendations. However, building comparisons at scale without pre-existing training/taxonomy data remains an…
Language in a (Search) Box: Grounding Language Learning in Real-World Human-Machine Interaction
Federico Bianchi, Ciro Greco, Jacopo Tagliabue
We investigate grounded language learning through real-world data, by modelling a teacher-learner dynamics through the natural interactions occurring between users and search engin…
Query2Prod2Vec Grounded Word Embeddings for eCommerce
Federico Bianchi, Jacopo Tagliabue, Bingqing Yu
We present Query2Prod2Vec, a model that grounds lexical representations for product search in product embeddings: in our model, meaning is a mapping between words and a latent spac…
SIGIR 2021 E-Commerce Workshop Data Challenge
Jacopo Tagliabue, Ciro Greco, Jean-Francis Roy +4
The 2021 SIGIR workshop on eCommerce is hosting the Coveo Data Challenge for "In-session prediction for purchase intent and recommendations". The challenge addresses the growing ne…