22,340 research outputs found
Synthetic sequence generator for recommender systems - memory biased random walk on sequence multilayer network
Personalized recommender systems rely on each user's personal usage data in
the system, in order to assist in decision making. However, privacy policies
protecting users' rights prevent these highly personal data from being publicly
available to a wider researcher audience. In this work, we propose a memory
biased random walk model on multilayer sequence network, as a generator of
synthetic sequential data for recommender systems. We demonstrate the
applicability of the synthetic data in training recommender system models for
cases when privacy policies restrict clickstream publishing.Comment: The new updated version of the pape
Synthetic Observational Health Data with GANs: from slow adoption to a boom in medical research and ultimately digital twins?
After being collected for patient care, Observational Health Data (OHD) can
further benefit patient well-being by sustaining the development of health
informatics and medical research. Vast potential is unexploited because of the
fiercely private nature of patient-related data and regulations to protect it.
Generative Adversarial Networks (GANs) have recently emerged as a
groundbreaking way to learn generative models that produce realistic synthetic
data. They have revolutionized practices in multiple domains such as
self-driving cars, fraud detection, digital twin simulations in industrial
sectors, and medical imaging.
The digital twin concept could readily apply to modelling and quantifying
disease progression. In addition, GANs posses many capabilities relevant to
common problems in healthcare: lack of data, class imbalance, rare diseases,
and preserving privacy. Unlocking open access to privacy-preserving OHD could
be transformative for scientific research. In the midst of COVID-19, the
healthcare system is facing unprecedented challenges, many of which of are data
related for the reasons stated above.
Considering these facts, publications concerning GAN applied to OHD seemed to
be severely lacking. To uncover the reasons for this slow adoption, we broadly
reviewed the published literature on the subject. Our findings show that the
properties of OHD were initially challenging for the existing GAN algorithms
(unlike medical imaging, for which state-of-the-art model were directly
transferable) and the evaluation synthetic data lacked clear metrics.
We find more publications on the subject than expected, starting slowly in
2017, and since then at an increasing rate. The difficulties of OHD remain, and
we discuss issues relating to evaluation, consistency, benchmarking, data
modelling, and reproducibility.Comment: 31 pages (10 in previous version), not including references and
glossary, 51 in total. Inclusion of a large number of recent publications and
expansion of the discussion accordingl
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