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pro vyhledávání: '"Chia, Patrick"'
Autor:
Bianchi, Federico, Chia, Patrick John, Yuksekgonul, Mert, Tagliabue, Jacopo, Jurafsky, Dan, Zou, James
Negotiation is the basis of social interactions; humans negotiate everything from the price of cars to how to share common resources. With rapidly growing interest in using large language models (LLMs) to act as agents on behalf of human users, such
Externí odkaz:
http://arxiv.org/abs/2402.05863
Autor:
Chia, Patrick John, Attanasio, Giuseppe, Tagliabue, Jacopo, Bianchi, Federico, Greco, Ciro, Moreira, Gabriel de Souza P., Eynard, Davide, Husain, Fahd
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 retention and fairness, often taking a back seat. Moreover, reconciling
Externí odkaz:
http://arxiv.org/abs/2304.10621
Autor:
Bianchi, Federico, Chia, Patrick John, Greco, Ciro, Pomo, Claudio, Moreira, Gabriel, Eynard, Davide, Husain, Fahd, Tagliabue, Jacopo
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 multitude of deployment scenarios. Recommender systems are often evalua
Externí odkaz:
http://arxiv.org/abs/2304.07145
Autor:
Tagliabue, Jacopo, Bianchi, Federico, Schnabel, Tobias, Attanasio, Giuseppe, Greco, Ciro, Moreira, Gabriel de Souza P., Chia, Patrick John
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 user interfaces. However, research focused almost exclusively on the
Externí odkaz:
http://arxiv.org/abs/2207.05772
Autor:
Chia, Patrick John, Attanasio, Giuseppe, Bianchi, Federico, Terragni, Silvia, Magalhães, Ana Rita, Goncalves, Diogo, Greco, Ciro, Tagliabue, Jacopo
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 learning problems, we argue that practitioners would greatly benefit from mor
Externí odkaz:
http://arxiv.org/abs/2204.03972
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 co
Externí odkaz:
http://arxiv.org/abs/2204.02473
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 undoubtedly nuanced: ad hoc error analysis and deployment-specific test
Externí odkaz:
http://arxiv.org/abs/2111.09963
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 open challenge, especially within the operational constraints of shop
Externí odkaz:
http://arxiv.org/abs/2107.03256
Autor:
Tagliabue, Jacopo, Greco, Ciro, Roy, Jean-Francis, Yu, Bingqing, Chia, Patrick John, Bianchi, Federico, Cassani, Giovanni
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 need for reliable predictions within the boundaries of a shopping sessio
Externí odkaz:
http://arxiv.org/abs/2104.09423
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