Matching Support Seekers with Support Providers and Responsible Decision Making with Knowledge-infused Bandits

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Super Excited to share news on the acceptance of two research ideas:

  • Matching Support Seekers with Support Providers
    • An automated method to identify supportive users is challenging due to diverse roles of users, but it is more challenging to #associate them with #supportseekers for suitable help.
    • Annotating such matches is exceptionally time consuming for experts and #moderators on online platforms. We demonstrate an expert validated methodology that uses #diverseknowledge to match support seekers and support providers.
    • 7.2 out of 10 confidence ratings from subject matter expert and users of Reddit for support
    • We tested our strategy on 25000 users from Coronavirus and covid19_support community pairs on Reddit who express anxiety and depression with certainty.
    • Preprint
    • Kudos to team: Kaushik Roy, Aditya Sharma (intern), Biplav Srivastava, Amit P. Sheth](https://sc.edu/study/colleges_schools/engineering_and_computing/faculty-staff/amitsheth.php)
    • Illustration
  • Knowledge Infused Policy Gradients with Upper Confidence Bound for Relational Bandits
    • Knowledge Infusion in the Relational Bandits learning setting.
    • Deriving a Upper Confidence Bound style strategy for effective explore-exploit trade-off.
    • Performing regret analysis of our approach when we include human expertise and extensive experimentation on several benchmarking datasets.
    • Preprint
    • Kudos to Team: Kaushik Roy, Qi Zhang, Amit P. Sheth