35 research outputs found
Digital Human Interactive Recommendation Decision-Making Based on Reinforcement Learning
Digital human recommendation system has been developed to help customers find
their favorite products and is playing an active role in various recommendation
contexts. How to timely catch and learn the dynamics of the preferences of the
customers, while meeting their exact requirements, becomes crucial in the
digital human recommendation domain. We design a novel practical digital human
interactive recommendation agent framework based on Reinforcement Learning(RL)
to improve the efficiency of the interactive recommendation decision-making by
leveraging both the digital human features and the superior flexibility of RL.
Our proposed framework learns through real-time interactions between the
digital human and customers dynamically through the state-of-art RL algorithms,
combined with multimodal embedding and graph embedding, to improve the accuracy
of personalization and thus enable the digital human agent to timely catch the
attention of the customer. Experiments on real business data demonstrate that
our framework can provide better personalized customer engagement and better
customer experiences.Comment: 9 pages, 1 figure, 1 table, the paper has been accepted and this is
the final camera-ready for NeurIPS 2022 Workshop on Human in the Loop
Learning, https://neurips-hill.github.io
Cost-Effective Incentive Allocation via Structured Counterfactual Inference
We address a practical problem ubiquitous in modern marketing campaigns, in
which a central agent tries to learn a policy for allocating strategic
financial incentives to customers and observes only bandit feedback. In
contrast to traditional policy optimization frameworks, we take into account
the additional reward structure and budget constraints common in this setting,
and develop a new two-step method for solving this constrained counterfactual
policy optimization problem. Our method first casts the reward estimation
problem as a domain adaptation problem with supplementary structure, and then
subsequently uses the estimators for optimizing the policy with constraints. We
also establish theoretical error bounds for our estimation procedure and we
empirically show that the approach leads to significant improvement on both
synthetic and real datasets
ABO genotype alters the gut microbiota by regulating GalNAc levels in pigs.
peer reviewedThe composition of the intestinal microbiome varies considerably between individuals and is correlated with health1. Understanding to what extend and how host genetics contributes to this variation is paramount yet has proven difficult as few associations have been replicated, particularly in humans2. We herein study the effect of host genotype on the composition of the intestinal microbiota in a large mosaic pig population. We show that, under conditions of exacerbated genetic diversity and environmental uniformity, microbiota composition and abundance of specific taxa are heritable. We map a quantitative trait locus affecting the abundance of Erysipelotrichaceae species and show that it is caused by a 2.3-Kb deletion in the N-acetyl-galactosaminyl-transferase gene underpinning the ABO blood group in humans. We show that this deletion is a ≥3.5 million years old trans-species polymorphism under balancing selection. We demonstrate that it decreases the concentrations of N-acetyl-galactosamine in the gut thereby reducing the abundance of Erysipelotrichaceae that can import and catabolize N-acetyl-galactosamine. Our results provide very strong evidence for an effect of host genotype on the abundance of specific bacteria in the intestine combined with insights in the molecular mechanisms that underpin this association. They pave the way towards identifying the same effect in human rural populations
Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial
Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials.
Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure.
Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen.
Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049