Results 41 to 50 of about 633,019 (195)

AI/ML Recommender System Interoperability Guideline

, 2023
This document describes the current architecture of the EOSC Recommendation System and provides guidelines for three integration options with external service components. The intended audience for the AI/ML Recommender System Interoperabilty Guideline is technical experts who would like learn how to design their services to be interoperable or ...
Wolski, Marcin, Martyn, Krzysztof, Łabędzki, Maciej, Xydas, Michail, Zamani, Themis, Walter, Bartosz, Shepherdson, John William, Kolomanski, Michal, Triantafyllis, Nikolaos   +8 more
openaire   +1 more source

AI-Powered Career Recommendation System

INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Abstract- In today’s competitive and rapidly evolving job market, selecting an appropriate career path is increasingly challenging for students and professionals. Traditional career guidance methods, often based on static aptitude tests, human counsellors, or outdated datasets, fail to account for dynamic industry trends, emerging job roles, and skill ...
Ashish Kumar Yadav, Shivangi Srivastava, Akash Vishwakarma, Prakhar Gupta, Karan Yadav, Viru Rajbhar   +5 more
openaire   +1 more source

An authentic human-like figure: the success keys of AI fashion influencer

Cogent Business & Management
Considering the advantages of using AI influencers compared to human influencers, fashion marketers increasingly using AI influencers to promote their products.
Jein Sriana Toyib, Widya Paramita
doaj   +1 more source

AI models for recommendation

, 2021
Ponencia presentada en EMAI2021, West Bengal, India, 4/4 ...
openaire   +1 more source

Invigorating continuance intention among users of AI chatbots in the banking industry: an empirical study from Tanzania

Cogent Business & Management
While there has been a significant increase in the use of artificial intelligence (AI) in marketing, limited research has focused on the user experience of AI-powered chatbots and their implications in developing countries.
Esther Ernest Mfumbilwa, Caroline Mnong’one, Emmanuel Chao, David Amani   +3 more
doaj   +1 more source

Exploiting synergy between ontologies and recommender systems [PDF]

, 2002
Recommender systems learn about user preferences over time, automatically finding things of similar interest. This reduces the burden of creating explicit queries.
Alani, Harith, Middleton, Stuart E., Roure, David C. De, Shadbolt, Nigel R.   +3 more
core   +2 more sources

Investigating the role of AI explanations in lay individuals' comprehension of radiology reports: A metacognition lens.

PLoS ONE
While there has been extensive research on techniques for explainable artificial intelligence (XAI) to enhance AI recommendations, the metacognitive processes in interacting with AI explanations remain underexplored.
Yegin Genc, Mehmet Eren Ahsen, Zhan Zhang   +2 more
doaj   +1 more source

Independent review of variable fees and student finance arrangements: report to Minister for Employment and Learning [PDF]

, 2010
"The report is structured as follows: Section 1 Introduction and Background to the Review, Section 2 Current Arrangements, Section 3 Impact of the Introduction of Variable Student Fees, Section 4 Future Fee Scenarios for Northern Ireland, Section 5 ...
Stuart, Joanne
core  

Recommendations for diabetic macular edema management by retina specialists and large language model-based artificial intelligence platforms

International Journal of Retina and Vitreous
Purpose To study the role of artificial intelligence (AI) in developing diabetic macular edema (DME) management recommendations by creating and comparing responses to clinicians in hypothetical AI-generated case scenarios. The study also examined whether
Ayushi Choudhary, Nikhil Gopalakrishnan, Aishwarya Joshi, Divya Balakrishnan, Jay Chhablani, Naresh Kumar Yadav, Nikitha Gurram Reddy, Padmaja Kumari Rani, Priyanka Gandhi, Rohit Shetty, Rupak Roy, Snehal Bavaskar, Vishma Prabhu, Ramesh Venkatesh   +13 more
doaj   +1 more source

Impacts of cognitive forcing and need for cognition on biased AI-assisted decision making about mental health emergencies

Scientific Reports
Artificial intelligence (AI) trained to predict psychiatric inpatient violence may overestimate risks for marginalized groups, making it critical to find ways to mitigate reliance on biased AI in this context. One potential solution is Cognitive forcing (
Shrika Vejandla, Aksharaa Ray, Laura Sikstrom, Matt Ratto, Juveria Zaheer, Marta M. Maslej   +5 more
doaj   +1 more source