Social Plane for Recommenders in Network Performance Expectation Management

Zhang, Yuanxun; Calyam, Prasad; Debroy, Saptarshi; Nuguri, Sai Shreya

doi:10.1109/tnsm.2017.2772905

Cited by 4 publications

(1 citation statement)

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“…The core of the system relies on active online community discussions that help refine the data and facilitate their expertise on high-quality information. The work in [26] integrates a social network that addresses the difficulty in knowledge sharing among experts to enable proper network performance expectation management. The lack of a social platform for sharing knowledge and working collaboratively makes the task of isolating and diagnosing network bottleneck events rapidly cumbersome.…”

Section: Expertise Sharing and Crowdsourcingmentioning

confidence: 99%

Evidence-Based Recommender System for a COVID-19 Publication Analytics Service

et al. 2021

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The rapid growth of COVID-19 publications has driven clinical researchers and healthcare professionals in pursuit to reduce the knowledge gap on reliable information for effective pandemic solutions. The manual task of retrieving high-quality publications based on the evidence pyramid levels, however, presents a major bottleneck in researchers' workflows. In this paper, we propose an "evidencebased" recommender system namely, KnowCOVID-19 that utilizes an edge computing service to integrate recommender modules for data analytics using end-user thin-clients. The edge computing service features chatbot-based web interface that handles a given COVID-19 publication dataset using two recommender system modules: (i) evidence-based filtering that observes domain specific topics across the literature and classifies the filtered information according to a clinical category, and (ii) social filtering that allows diverse experts with similar objectives to collaborate via a "social plane" to jointly find answers to critical clinical questions to fight the pandemic. We compare the Domain-specific Topic Model (DSTM) used in our evidence-based filtering with state-of-the-art models considering the CORD-19 dataset (a COVID-19 publication archive) and show improved generalization effectiveness as well as knowledge pattern query effectiveness. In addition, we conduct a comparison study between a manual literature review process and the KnowCOVID-19 augmented process, and evaluate the benefits of our information retrieval techniques over important queries provided by COVID-19 clinical experts.

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Section: Expertise Sharing and Crowdsourcingmentioning

confidence: 99%