Community-based Cyberreading for Information Understanding

TL;DR

This paper proposes community-based cyberreading methods that facilitate physical and virtual collaboration among scholars to enhance understanding of scientific publications through grouping, clustering, and personalized resource recommendation.

Contribution

It introduces novel algorithms for grouping readers and clustering based on information needs, along with learning to rank models for resource recommendation.

Findings

Effective grouping of readers based on profiles and behavior

Clustering of readers by information needs improves targeted support

Personalized resource recommendations aid understanding

Abstract

Although the content in scientific publications is increasingly challenging, it is necessary to investigate another important problem, that of scientific information understanding. For this proposed problem, we investigate novel methods to assist scholars (readers) to better understand scientific publications by enabling physical and virtual collaboration. For physical collaboration, an algorithm will group readers together based on their profiles and reading behavior, and will enable the cyberreading collaboration within a online reading group. For virtual collaboration, instead of pushing readers to communicate with others, we cluster readers based on their estimated information needs. For each cluster, a learning to rank model will be generated to recommend readers' communitized resources (i.e., videos, slides, and wikis) to help them understand the target publication.