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PerSummRe: Gaze-Based Personalized Summary Recommendation Tool for Wikipedia

PerSummRe: Gaze-Based Personalized Summary Recommendation Tool for Wikipedia

Neeru Dubey, Amit Arjun Verma, Simran Setia, S. R. S. Iyengar
Copyright: © 2022 |Volume: 24 |Issue: 3 |Pages: 18
ISSN: 1548-7717|EISSN: 1548-7725|EISBN13: 9781799878223|DOI: 10.4018/JCIT.20220701.oa7
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MLA

Dubey, Neeru, et al. "PerSummRe: Gaze-Based Personalized Summary Recommendation Tool for Wikipedia." JCIT vol.24, no.3 2022: pp.1-18. http://doi.org/10.4018/JCIT.20220701.oa7

APA

Dubey, N., Verma, A. A., Setia, S., & Iyengar, S. R. (2022). PerSummRe: Gaze-Based Personalized Summary Recommendation Tool for Wikipedia. Journal of Cases on Information Technology (JCIT), 24(3), 1-18. http://doi.org/10.4018/JCIT.20220701.oa7

Chicago

Dubey, Neeru, et al. "PerSummRe: Gaze-Based Personalized Summary Recommendation Tool for Wikipedia," Journal of Cases on Information Technology (JCIT) 24, no.3: 1-18. http://doi.org/10.4018/JCIT.20220701.oa7

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Abstract

The size of Wikipedia grows exponentially every year, due to which users face the problem of information overload. We purpose a remedy to this problem by developing a recommendation system for Wikipedia articles. The proposed technique automatically generates a personalized synopsis of the article that a user aims to read next. We develop a tool, called PerSummRe, which learns the reading preferences of a user through a vision-based analysis of his/her past reads. We use an ensemble non-invasive eye gaze tracking technique to analyze user’s reading pattern. This tool performs user profiling and generates a recommended personalized summary of yet unread Wikipedia article for a user. Experimental results showcase the efficiency of the recommendation technique.