Zobrazeno 1 - 10
of 1 801
pro vyhledávání: '"Ricci, Francesco"'
Autor:
Deldjoo, Yashar, He, Zhankui, McAuley, Julian, Korikov, Anton, Sanner, Scott, Ramisa, Arnau, Vidal, Rene, Sathiamoorthy, Maheswaran, Kasrizadeh, Atoosa, Milano, Silvia, Ricci, Francesco
Generative models are a class of AI models capable of creating new instances of data by learning and sampling from their statistical distributions. In recent years, these models have gained prominence in machine learning due to the development of app
Externí odkaz:
http://arxiv.org/abs/2409.15173
Autor:
Ramisa, Arnau, Vidal, Rene, Deldjoo, Yashar, He, Zhankui, McAuley, Julian, Korikov, Anton, Sanner, Scott, Sathiamoorthy, Mahesh, Kasrizadeh, Atoosa, Milano, Silvia, Ricci, Francesco
Many recommendation systems limit user inputs to text strings or behavior signals such as clicks and purchases, and system outputs to a list of products sorted by relevance. With the advent of generative AI, users have come to expect richer levels of
Externí odkaz:
http://arxiv.org/abs/2409.10993
Autor:
Korikov, Anton, Sanner, Scott, Deldjoo, Yashar, He, Zhankui, McAuley, Julian, Ramisa, Arnau, Vidal, Rene, Sathiamoorthy, Mahesh, Kasrizadeh, Atoosa, Milano, Silvia, Ricci, Francesco
While previous chapters focused on recommendation systems (RSs) based on standardized, non-verbal user feedback such as purchases, views, and clicks -- the advent of LLMs has unlocked the use of natural language (NL) interactions for recommendation.
Externí odkaz:
http://arxiv.org/abs/2408.10946
Publikováno v:
ACM Trans. Interact. Intell. Syst. (January 2024)
Group recommender systems (GRSs) identify items to recommend to a group of people by aggregating group members' individual preferences into a group profile, and selecting the items that have the largest score in the group profile. The GRS predicts th
Externí odkaz:
http://arxiv.org/abs/2308.03083
Autor:
Donadello, Ivan, Di Francescomarino, Chiara, Maggi, Fabrizio Maria, Ricci, Francesco, Shikhizada, Aladdin
Prescriptive Process Monitoring systems recommend, during the execution of a business process, interventions that, if followed, prevent a negative outcome of the process. Such interventions have to be reliable, that is, they have to guarantee the ach
Externí odkaz:
http://arxiv.org/abs/2211.04880
Recommender systems (RSs) aim to help users to effectively retrieve items of their interests from a large catalogue. For a quite long period of time, researchers and practitioners have been focusing on developing accurate RSs. Recent years have witne
Externí odkaz:
http://arxiv.org/abs/2208.06265
Autor:
Branchi, Stefano, Di Francescomarino, Chiara, Ghidini, Chiara, Massimo, David, Ricci, Francesco, Ronzani, Massimiliano
The rise of process data availability has recently led to the development of data-driven learning approaches. However, most of these approaches restrict the use of the learned model to predict the future of ongoing process executions. The goal of thi
Externí odkaz:
http://arxiv.org/abs/2203.15398
Autor:
Farris, Roberta, Ricci, Francesco, Casu, Giulio, Dahliah, Diana, Hautier, Geoffroy, Rignanese, Gian-Marco, Fiorentini, Vincenzo
Publikováno v:
Physical Review Materials 5, 125406 (2021)
We report a giant thermoelectric figure of merit $ZT$ (up to 6 at 1100 K) in $n$-doped lanthanum oxysulphate LaSO. Thermoelectric coefficients are computed from ab initio bands within Bloch-Boltzmann theory in an energy-, chemical potential- and temp
Externí odkaz:
http://arxiv.org/abs/2111.13874