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Found 9 papers, 1 papers with code
AiGen-FoodReview: A Multimodal Dataset of Machine-Generated Restaurant Reviews and Images on Social Media
The paper contributes by open-sourcing the dataset and releasing fake review detectors, recommending its use in unimodal and multimodal fake review detection tasks, and evaluating linguistic and visual features in synthetic versus authentic data.
Dissecting Medical Referral Mechanisms in Health Services: Role of Physician Professional Networks
Medical referrals between primary care physicians (PC) and specialist care (SC) physicians profoundly impact patient care regarding quality, satisfaction, and cost.
Extreme Multilabel Classification for Specialist Doctor Recommendation with Implicit Feedback and Limited Patient Metadata
Our research focuses on medical referrals and aims to predict recommendations in different specialties of physicians for both new patients and those with a consultation history.
Beyond Rankings: Exploring the Impact of SERP Features on Organic Click-through Rates
Search Engine Result Pages (SERPs) serve as the digital gateways to the vast expanse of the internet.
Interpretable Deep Learning for Forecasting Online Advertising Costs: Insights from the Competitive Bidding Landscape
As advertisers increasingly shift their budgets toward digital advertising, forecasting advertising costs is essential for making budget plans to optimize marketing campaign returns.
Combat AI With AI: Counteract Machine-Generated Fake Restaurant Reviews on Social Media
Recent advances in generative models such as GPT may be used to fabricate indistinguishable fake customer reviews at a much lower cost, thus posing challenges for social media platforms to detect these machine-generated fake reviews.
Online Advertising Revenue Forecasting: An Interpretable Deep Learning Approach
Online advertising revenues account for an increasing share of publishers' revenue streams, especially for small and medium-sized publishers who depend on the advertisement networks of tech companies such as Google and Facebook.
Incorporating Domain Knowledge into Health Recommender Systems using Hyperbolic Embeddings
In contrast to many other domains, recommender systems in health services may benefit particularly from the incorporation of health domain knowledge, as it helps to provide meaningful and personalised recommendations catering to the individual's health needs.
A Hybrid Recommender System for Patient-Doctor Matchmaking in Primary Care
We partner with a leading European healthcare provider and design a mechanism to match patients with family doctors in primary care.
