24 citations · 34 across the 4 of their papers we have counts for
5 papers · 1 filter
"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…
How big can style be? Addressing high dimensionality for recommending with style
Diogo Goncalves, Liweu Liu, Ana Magalhães
Using embeddings as representations of products is quite commonplace in recommender systems, either by extracting the semantic embeddings of text descriptions, user sessions, colla…
A Large-Scale Characterization of User Behaviour in Cable TV
Diogo Goncalves, Miguel Costa, Francisco M. Couto
Nowadays, Cable TV operators provide their users multiple ways to watch TV content, such as Live TV and Video on Demand (VOD) services. In the last years, Catch-up TV has been intr…
A Flexible Recommendation System for Cable TV
Diogo Goncalves, Miguel Costa, Francisco M. Couto
Recommendation systems are being explored by Cable TV operators to improve user satisfaction with services, such as Live TV and Video on Demand (VOD) services. More recently, Catch…