298 citations · 754 across the 33 of their papers we have counts for
10 papers · 1 filter
E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender Systems
Patrick John Chia, Giuseppe Attanasio, Jacopo Tagliabue +5
Recommender Systems today are still mostly evaluated in terms of accuracy, with other aspects beyond the immediate relevance of recommendations, such as diversity, long-term user r…
EvalRS 2023. Well-Rounded Recommender Systems For Real-World Deployments
Federico Bianchi, Patrick John Chia, Ciro Greco +5
EvalRS aims to bring together practitioners from industry and academia to foster a debate on rounded evaluation of recommender systems, with a focus on real-world impact across a m…
EvalRS: a Rounded Evaluation of Recommender Systems
Jacopo Tagliabue, Federico Bianchi, Tobias Schnabel +4
Much of the complexity of Recommender Systems (RSs) comes from the fact that they are used as part of more complex applications and affect user experience through a varied range of…
"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…
Contrastive language and vision learning of general fashion concepts
Patrick John Chia, Giuseppe Attanasio, Federico Bianchi +5
The steady rise of online shopping goes hand in hand with the development of increasingly complex ML and NLP models. While most use cases are cast as specialized supervised learnin…
Beyond NDCG: behavioral testing of recommender systems with RecList
Patrick John Chia, Jacopo Tagliabue, Federico Bianchi +2
As with most Machine Learning systems, recommender systems are typically evaluated through performance metrics computed over held-out data points. However, real-world behavior is u…
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…
Fantastic Embeddings and How to Align Them: Zero-Shot Inference in a Multi-Shop Scenario
Federico Bianchi, Jacopo Tagliabue, Bingqing Yu +2
This paper addresses the challenge of leveraging multiple embedding spaces for multi-shop personalization, proving that zero-shot inference is possible by transferring shopping int…
"An Image is Worth a Thousand Features": Scalable Product Representations for In-Session Type-Ahead Personalization
Bingqing Yu, Jacopo Tagliabue, Ciro Greco +1
We address the problem of personalizing query completion in a digital commerce setting, in which the bounce rate is typically high and recurring users are rare. We focus on in-sess…