Proceedings ArticleDOI
Fuzzy-Based Conversational Recommender for Data-intensive Science Gateway Applications
Arjun Ankathatti Chandrashekara,Radha Krishna Murthy Talluri,Sai Swathi Sivarathri,Reshmi Mitra,Prasad Calyam,Kerk F. Kee,Satish S. Nair +6 more
- pp 4870-4875
TLDR
A novel intuitionistic fuzzy logic based conversational recommender that can provide guidance to users when using science gateways for research and education workflows and can provide step-by-step navigational support and generate distinct responses based on user proficiency is presented.Abstract:
Neuro-scientists are increasingly relying on parallel and distributed computing resources for analysis and visualization of their neuron simulations. Although science gateways have democratized relevant high performance/throughput resources, users require expert knowledge about programming and infrastructure configuration that is beyond the repertoire of most neuroscience programs. These factors become deterrents for the successful adoption and the ultimate diffusion (i.e., systemic spread) of science gateways in the neuroscience community. In this paper, we present a novel intuitionistic fuzzy logic based conversational recommender that can provide guidance to users when using science gateways for research and education workflows. The users interact with a context-aware chatbot that is embedded within custom web-portals to obtain simulation tools/resources to accomplish their goals. In order to ensure user goals are met, the chatbot profiles a user’s cyberinfrastructure and neuroscience domain proficiency level using a ‘usability quadrant’ approach. Simulation of user queries for an exemplary neuroscience use case demonstrates that our chatbot can provide step-by-step navigational support and generate distinct responses based on user proficiency.read more
Citations
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Journal ArticleDOI
A Survey on Conversational Recommender Systems
TL;DR: A detailed survey of existing approaches to conversational recommendation is provided, categorizing these approaches in various dimensions, e.g., in terms of the supported user intents or the knowledge they use in the background.
Journal ArticleDOI
A Survey on Conversational Recommender Systems
TL;DR: Conversational recommender systems (CRS) as mentioned in this paper are software applications that help users to find items of interest in situations of information overload, where the user can ask questions about the recommendations and to give feedback.
Journal ArticleDOI
Evidence-Based Recommender System for a COVID-19 Publication Analytics Service
Roland Oruche,Vidya Gundlapalli,Aditya P. Biswal,Prasad Calyam,Mauro Lemus Alarcon,Yuanxun Zhang,Naga Ramya Bhamidipati,Abhiram Malladi,Hariharan Regunath +8 more
TL;DR: KnowCOVID-19 as mentioned in this paper is an evidence-based recommender system that utilizes an edge computing service to integrate recommender modules for data analytics using end-user thin-clients.
Journal ArticleDOI
If you build it, promote it, and they trust you, then they will come: Diffusion strategies for science gateways and cyberinfrastructure adoption to harness big data in the science, technology, engineering, and mathematics (STEM) community
TL;DR: This article identified seven external communication practices of science gateways and cyberinfrastructure projects and revised the pop culture line to “If You Build It, Promote It, and They Trust You, Then They Will Come.”
Journal ArticleDOI
Recommender‐as‐a‐service with chatbot guided domain‐science knowledge discovery in a science gateway
Komal Bhupendra Vekaria,Prasad Calyam,Sai Swathi Sivarathri,Songjie Wang,Yuanxun Zhang,Ashish Pandey,Cong Chen,Dong Xu,Trupti Joshi,Satish S. Nair +9 more
TL;DR: The OnTimeRecommend comprises of several integrated recommender modules implemented as microservices that can be augmented to a science gateway in the form of a recommender-as-a-service and is aided by a chatbot plug-in viz., Vidura Advisor.
References
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