Recommendation of newly published research papers using belief propagation

Jiwoon Ha, Soon Hyoung Kwon, Sang Wook Kim, Dongwon Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

Abstract

The problem to retrieve most relevant research papers for a given academic is studied. Existing solutions cannot adequately address the recommendation of new papers due to their lack of history information, the so-called cold start problem. Using the graphical model built from citation information between a new paper pi and published papers, toward this challenge, we propose a novel approach based on a probabilistic inference algorithm, the Belief Propagation (BP), to predict the likelihood of pi's relevance to a target academic. Compared to item-based collaborative filtering method using a DBLP data set, the empirical validation shows an improvement in accuracy up to 26% in F1 score.

Original languageEnglish (US)
Title of host publicationProceedings of the 2014 Research in Adaptive and Convergent Systems, RACS 2014
PublisherAssociation for Computing Machinery
Pages77-81
Number of pages5
ISBN (Electronic)9781450330602
DOIs
StatePublished - Oct 5 2014
Event2014 Conference on Research in Adaptive and Convergent Systems, RACS 2014 - Towson, United States
Duration: Oct 5 2014Oct 8 2014

Publication series

NameProceedings of the 2014 Research in Adaptive and Convergent Systems, RACS 2014

Other

Other2014 Conference on Research in Adaptive and Convergent Systems, RACS 2014
Country/TerritoryUnited States
CityTowson
Period10/5/1410/8/14

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science(all)

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