Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Brian St. Thomas"'
Publikováno v:
Journal of Computational and Graphical Statistics. 31:337-350
Autor:
Praveen Chandar, Brian St. Thomas, Lucas Maystre, Vijay Pappu, Roberto Sanchis-Ojeda, Tiffany Wu, Ben Carterette, Mounia Lalmas, Tony Jebara
Publikováno v:
Proceedings of the ACM Web Conference 2022.
Publikováno v:
KDD
Designers of online search and recommendation services often need to develop metrics to assess system performance. This tutorial focuses on mixed methods approaches to developing user-focused evaluation metrics. This starts with choosing how data is
Publikováno v:
SIGIR
Instant search has become a popular search paradigm in which users are shown a new result page in response to every keystroke triggered. Over recent years, the paradigm has been widely adopted in several domains including personal email search, e-com
Autor:
Ang Li, Brian St. Thomas, Jennifer Thom, Praveen Chandar, Jean Garcia-Gathright, Christine Hosey
Publikováno v:
WWW
Music listening is a commonplace activity that has transformed as users engage with online streaming platforms. When presented with anytime, anywhere access to a vast catalog of music, users face challenges in searching for what they want to hear. We
Publikováno v:
CHI
Music-streaming platforms offer users a large amount of content for consumption. Finding the right music can be challenging and users often need to search through extensive catalogs provided by these platforms. Prior research has focused on general-d
Publikováno v:
RecSys
Evaluation is a fundamental part of a recommendation system. Evaluation typically takes one of three forms: (1) smaller lab studies with real users; (2) batch tests with offline collections, judgements, and measures; (3) large-scale controlled experi
Publikováno v:
SIGIR
We study the use and evaluation of a system for supporting music discovery, the experience of finding and listening to content previously unknown to the user. We adopt a mixed methods approach, including interviews, unsupervised learning, survey rese
We propose an extrinsic regression framework for modeling data with manifold valued responses and Euclidean predictors. Regression with manifold responses has wide applications in shape analysis, neuroscience, medical imaging, and many other areas. O
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::97fe8b7f9b78498525ae12bb6a4ca357