5 research outputs found
Pregnancy Rates Following Transfer of Cultured Versus Non Cultured Frozen Thawed Human Embryos
TransAct: Transformer-based Realtime User Action Model for Recommendation at Pinterest
Sequential models that encode user activity for next action prediction have
become a popular design choice for building web-scale personalized
recommendation systems. Traditional methods of sequential recommendation either
utilize end-to-end learning on realtime user actions, or learn user
representations separately in an offline batch-generated manner. This paper (1)
presents Pinterest's ranking architecture for Homefeed, our personalized
recommendation product and the largest engagement surface; (2) proposes
TransAct, a sequential model that extracts users' short-term preferences from
their realtime activities; (3) describes our hybrid approach to ranking, which
combines end-to-end sequential modeling via TransAct with batch-generated user
embeddings. The hybrid approach allows us to combine the advantages of
responsiveness from learning directly on realtime user activity with the
cost-effectiveness of batch user representations learned over a longer time
period. We describe the results of ablation studies, the challenges we faced
during productionization, and the outcome of an online A/B experiment, which
validates the effectiveness of our hybrid ranking model. We further demonstrate
the effectiveness of TransAct on other surfaces such as contextual
recommendations and search. Our model has been deployed to production in
Homefeed, Related Pins, Notifications, and Search at Pinterest.Comment: \c{opyright} {ACM} {2023}. This is the author's version of the work.
It is posted here for your personal use. Not for redistribution. The
definitive Version of Record was published in KDD'23,
http://dx.doi.org/10.1145/3580305.359991
Harmonising knowledge for safer materials via the “NanoCommons” Knowledge Base
In mediaeval Europe, the term “commons” described the way that communities managed land that was held “in common” and provided a clear set of rules for how this “common land” was used and developed by, and for, the community. Similarly, as we move towards an increasingly knowledge-based society where data is the new oil, new approaches to sharing and jointly owning publicly funded research data are needed to maximise its added value. Such common management approaches will extend the data’s useful life and facilitate its reuse for a range of additional purposes, from modelling, to meta-analysis to regulatory risk assessment as examples relevant to nanosafety data. This “commons” approach to nanosafety data and nanoinformatics infrastructure provision, co-development, and maintenance is at the heart of the “NanoCommons” project and underpins its post-funding transition to providing a basis on which other initiatives and projects can build. The present paper summarises part of the NanoCommons infrastructure called the NanoCommons Knowledge Base. It provides interoperability for nanosafety data sources and tools, on both semantic and technical levels. The NanoCommons Knowledge Base connects knowledge and provides both programmatic (via an Application Programming Interface) and a user-friendly graphical interface to enable (and democratise) access to state of the art tools for nanomaterials safety prediction, NMs design for safety and sustainability, and NMs risk assessment, as well. In addition, the standards and interfaces for interoperability, e.g., file templates to contribute data to the NanoCommons, are described, and a snapshot of the range and breadth of nanoinformatics tools and models that have already been integrated are presented Finally, we demonstrate how the NanoCommons Knowledge Base can support users in the FAIRification of their experimental workflows and how the NanoCommons Knowledge Base itself has progressed towards richer compliance with the FAIR principles