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Fueling AI innovation
Behind the greatest discoveries lie collaboration and community. That’s why we believe in sharing our latest research on AI, NLP, and information retrieval.
Our research areas
Our mission is to develop AI applied to key business problems while pursuing bold ideas in key areas, from natural language (NLP/NLU) to machine learning operations (MLOps).
NLP/NLU
Personalization
Recommendations
Search
MLOps
Publications
Stay up to date with the latest findings from our research team.
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You Do Not Need a Bigger Boat: Recommendations at Reasonable Scale in a (Mostly) Serverless and Open Stack
Jacopo Tagliabue | 2021 | RecSys
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Jacopo Tagliabue, Federico Bianchi, Bingqing Yu | 2021 | Association for Computational Linguistics
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Patrick John Chia, Bingqing Yu, Jacopo Tagliabue | 2021 | SIGIR eCom
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Jacopo Tagliabue, Ciro Greco, Jean-Francis Roy, Bingqing Yu, Patrick John Chia, Federico Bianchi, Giovanni Cassani | 2021 | SIGIR eCom
Datasets
Download curated and anonymized datasets we release to help advance AI and democratize research.
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DATASET SIGIR eCOM 2021 Data Challenge Dataset We hosted the 2021 SIGIR eCom Data Challenge. This is the dataset containing utility scripts and information about data preparation and testing for the Challenge.
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DATASET Fantastic Embeddings and How to Align Them: Zero-Shot Inference in a Multi-Shop Scenario Dataset released with our SIGIR eCom 2020 paper titled “Fantastic Embeddings and How to Align Them: Zero-Shot Inference in a Multi-Shop Scenario”.
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DATASET
Shopper Intent Prediction from Clickstream Ecommerce Data with Minimal Browsing Information Dataset released in conjunction with our paper titled “Shopper Intent Prediction From Clickstream Ecommerce Data with Minimal Browsing Information”.
