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

5 Citations (Scopus)

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, Inc
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
CountryUnited States
CityTowson
Period10/5/1410/8/14

Fingerprint

Collaborative filtering

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science(all)

Cite this

Ha, J., Kwon, S. H., Kim, S. W., & Lee, D. (2014). Recommendation of newly published research papers using belief propagation. In Proceedings of the 2014 Research in Adaptive and Convergent Systems, RACS 2014 (pp. 77-81). (Proceedings of the 2014 Research in Adaptive and Convergent Systems, RACS 2014). Association for Computing Machinery, Inc. https://doi.org/10.1145/2663761.2664211
Ha, Jiwoon ; Kwon, Soon Hyoung ; Kim, Sang Wook ; Lee, Dongwon. / Recommendation of newly published research papers using belief propagation. Proceedings of the 2014 Research in Adaptive and Convergent Systems, RACS 2014. Association for Computing Machinery, Inc, 2014. pp. 77-81 (Proceedings of the 2014 Research in Adaptive and Convergent Systems, RACS 2014).
@inproceedings{8bf26f2f863b44ffa4afe52688baef60,
title = "Recommendation of newly published research papers using belief propagation",
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.",
author = "Jiwoon Ha and Kwon, {Soon Hyoung} and Kim, {Sang Wook} and Dongwon Lee",
year = "2014",
month = "10",
day = "5",
doi = "10.1145/2663761.2664211",
language = "English (US)",
series = "Proceedings of the 2014 Research in Adaptive and Convergent Systems, RACS 2014",
publisher = "Association for Computing Machinery, Inc",
pages = "77--81",
booktitle = "Proceedings of the 2014 Research in Adaptive and Convergent Systems, RACS 2014",

}

Ha, J, Kwon, SH, Kim, SW & Lee, D 2014, Recommendation of newly published research papers using belief propagation. in Proceedings of the 2014 Research in Adaptive and Convergent Systems, RACS 2014. Proceedings of the 2014 Research in Adaptive and Convergent Systems, RACS 2014, Association for Computing Machinery, Inc, pp. 77-81, 2014 Conference on Research in Adaptive and Convergent Systems, RACS 2014, Towson, United States, 10/5/14. https://doi.org/10.1145/2663761.2664211

Recommendation of newly published research papers using belief propagation. / Ha, Jiwoon; Kwon, Soon Hyoung; Kim, Sang Wook; Lee, Dongwon.

Proceedings of the 2014 Research in Adaptive and Convergent Systems, RACS 2014. Association for Computing Machinery, Inc, 2014. p. 77-81 (Proceedings of the 2014 Research in Adaptive and Convergent Systems, RACS 2014).

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

TY - GEN

T1 - Recommendation of newly published research papers using belief propagation

AU - Ha, Jiwoon

AU - Kwon, Soon Hyoung

AU - Kim, Sang Wook

AU - Lee, Dongwon

PY - 2014/10/5

Y1 - 2014/10/5

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84909991246&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84909991246&partnerID=8YFLogxK

U2 - 10.1145/2663761.2664211

DO - 10.1145/2663761.2664211

M3 - Conference contribution

AN - SCOPUS:84909991246

T3 - Proceedings of the 2014 Research in Adaptive and Convergent Systems, RACS 2014

SP - 77

EP - 81

BT - Proceedings of the 2014 Research in Adaptive and Convergent Systems, RACS 2014

PB - Association for Computing Machinery, Inc

ER -

Ha J, Kwon SH, Kim SW, Lee D. Recommendation of newly published research papers using belief propagation. In Proceedings of the 2014 Research in Adaptive and Convergent Systems, RACS 2014. Association for Computing Machinery, Inc. 2014. p. 77-81. (Proceedings of the 2014 Research in Adaptive and Convergent Systems, RACS 2014). https://doi.org/10.1145/2663761.2664211