Incorporating full text and bibliographic features to improve scholarly journal recommendation

Tirthankar Ghosal; Ananya Chakraborty; Ravi Sonam; Asif Ekbal; Sriparna Saha; Pushpak Bhattacharyya

doi:10.1109/JCDL.2019.00077

Incorporating full text and bibliographic features to improve scholarly journal recommendation

Tirthankar Ghosal, Ananya Chakraborty, Ravi Sonam, Asif Ekbal, Sriparna Saha, Pushpak Bhattacharyya

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

15 Scopus citations

Abstract

Selecting an appropriate venue to communicate one's research is the very first step in scholarly communication. Many papers are simply rejected from the editor's desk on not being submitted to the right journal. Existing journal recommender systems extract keywords only from the title and abstract sections of candidate articles and produce journal recommendations based on their weighted-match with a domain-specific vocabulary. Here in this work, we investigate a simple yet effective approach by incorporating additional information from bibliography and body section of academic manuscripts and show their potency to yield a better recommendation. On a closed set of ten different journals, our content-based recommender achieves significant improvement over the usual baselines (at least ~ 10%). Our preliminary approach is simple yet promising and if suitably applied could efficiently recommend journals from a larger pool as well.

Original language	English
Title of host publication	Proceedings - 2019 ACM/IEEE Joint Conference on Digital Libraries, JCDL 2019
Editors	Maria Bonn, Dan Wu, Stephen J. Downie, Alain Martaus
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	374-375
Number of pages	2
ISBN (Electronic)	9781728115474
DOIs	https://doi.org/10.1109/JCDL.2019.00077
State	Published - Jun 2019
Externally published	Yes
Event	19th ACM/IEEE Joint Conference on Digital Libraries, JCDL 2019 - Urbana-Champaign, United States Duration: Jun 2 2019 → Jun 6 2019

Publication series

Name	Proceedings of the ACM/IEEE Joint Conference on Digital Libraries
Volume	2019-June
ISSN (Print)	1552-5996

Conference

Conference	19th ACM/IEEE Joint Conference on Digital Libraries, JCDL 2019
Country/Territory	United States
City	Urbana-Champaign
Period	06/2/19 → 06/6/19

Funding

ACKNOWLEDGMENTS The first and fourth author gratefully acknowledge Visvesvaraya Ph.D. Scheme and YFRF under Ministry of Electronics and Information Technology (MeitY), Government of India for supporting this research. REFERENCES

Keywords

Full text processing
Journal recommendation
Scholarly communications

Access to Document

10.1109/JCDL.2019.00077

Cite this

Ghosal, T., Chakraborty, A., Sonam, R., Ekbal, A., Saha, S., & Bhattacharyya, P. (2019). Incorporating full text and bibliographic features to improve scholarly journal recommendation. In M. Bonn, D. Wu, S. J. Downie, & A. Martaus (Eds.), Proceedings - 2019 ACM/IEEE Joint Conference on Digital Libraries, JCDL 2019 (pp. 374-375). Article 8791200 (Proceedings of the ACM/IEEE Joint Conference on Digital Libraries; Vol. 2019-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/JCDL.2019.00077

Ghosal, Tirthankar ; Chakraborty, Ananya ; Sonam, Ravi et al. / Incorporating full text and bibliographic features to improve scholarly journal recommendation. Proceedings - 2019 ACM/IEEE Joint Conference on Digital Libraries, JCDL 2019. editor / Maria Bonn ; Dan Wu ; Stephen J. Downie ; Alain Martaus. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 374-375 (Proceedings of the ACM/IEEE Joint Conference on Digital Libraries).

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title = "Incorporating full text and bibliographic features to improve scholarly journal recommendation",

abstract = "Selecting an appropriate venue to communicate one's research is the very first step in scholarly communication. Many papers are simply rejected from the editor's desk on not being submitted to the right journal. Existing journal recommender systems extract keywords only from the title and abstract sections of candidate articles and produce journal recommendations based on their weighted-match with a domain-specific vocabulary. Here in this work, we investigate a simple yet effective approach by incorporating additional information from bibliography and body section of academic manuscripts and show their potency to yield a better recommendation. On a closed set of ten different journals, our content-based recommender achieves significant improvement over the usual baselines (at least ~ 10%). Our preliminary approach is simple yet promising and if suitably applied could efficiently recommend journals from a larger pool as well.",

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Ghosal, T, Chakraborty, A, Sonam, R, Ekbal, A, Saha, S & Bhattacharyya, P 2019, Incorporating full text and bibliographic features to improve scholarly journal recommendation. in M Bonn, D Wu, SJ Downie & A Martaus (eds), Proceedings - 2019 ACM/IEEE Joint Conference on Digital Libraries, JCDL 2019., 8791200, Proceedings of the ACM/IEEE Joint Conference on Digital Libraries, vol. 2019-June, Institute of Electrical and Electronics Engineers Inc., pp. 374-375, 19th ACM/IEEE Joint Conference on Digital Libraries, JCDL 2019, Urbana-Champaign, United States, 06/2/19. https://doi.org/10.1109/JCDL.2019.00077

Incorporating full text and bibliographic features to improve scholarly journal recommendation. / Ghosal, Tirthankar; Chakraborty, Ananya; Sonam, Ravi et al.
Proceedings - 2019 ACM/IEEE Joint Conference on Digital Libraries, JCDL 2019. ed. / Maria Bonn; Dan Wu; Stephen J. Downie; Alain Martaus. Institute of Electrical and Electronics Engineers Inc., 2019. p. 374-375 8791200 (Proceedings of the ACM/IEEE Joint Conference on Digital Libraries; Vol. 2019-June).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Selecting an appropriate venue to communicate one's research is the very first step in scholarly communication. Many papers are simply rejected from the editor's desk on not being submitted to the right journal. Existing journal recommender systems extract keywords only from the title and abstract sections of candidate articles and produce journal recommendations based on their weighted-match with a domain-specific vocabulary. Here in this work, we investigate a simple yet effective approach by incorporating additional information from bibliography and body section of academic manuscripts and show their potency to yield a better recommendation. On a closed set of ten different journals, our content-based recommender achieves significant improvement over the usual baselines (at least ~ 10%). Our preliminary approach is simple yet promising and if suitably applied could efficiently recommend journals from a larger pool as well.

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Ghosal T, Chakraborty A, Sonam R, Ekbal A, Saha S, Bhattacharyya P. Incorporating full text and bibliographic features to improve scholarly journal recommendation. In Bonn M, Wu D, Downie SJ, Martaus A, editors, Proceedings - 2019 ACM/IEEE Joint Conference on Digital Libraries, JCDL 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 374-375. 8791200. (Proceedings of the ACM/IEEE Joint Conference on Digital Libraries). doi: 10.1109/JCDL.2019.00077

Incorporating full text and bibliographic features to improve scholarly journal recommendation

Abstract

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Keywords

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