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FPSRS: a fusion approach for paper submission recommendation system

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Abstract

Recommender systems have been increasingly popular in entertainment and consumption, and they are evident in academics, especially for applications that suggest submitting scientific articles to scientists. However, due to the various acceptance rates, impact factors, and rankings in different publishers, searching for a proper venue or journal to submit a scientific work usually takes a lot of time and effort. In this paper, we aim to present two new approaches extended from our paper (Huynh et al. 2021) presented at IAE/AIE 2021. In the first approach, we employ RNN structures besides using Conv1D. In the second approach, we introduce a new method, using DistillBert for two cases of uppercase and lowercase words. It can help vectorize features (such as Title, Abstract, and Keywords) and then use Conv1d to perform feature extraction. Furthermore, we propose a new calculation method for similarity score for Aim & Scope with other features (we name this approach is DistilBertAims). It can help keep the weights of similarity score calculation continuously updated and then continue to fit more data. The experimental results show that the second approach could obtain a better performance, which are 62.46%, 90.32%, 94.89%, 97.96% while the best performance in previous studies barely gained 50.02%, 78.89%, 86.27%, 93.23% in terms of Top K Accuracy (K = 1, 3, 5, 10). Interestingly, our best approach in this paper is higher than 12.44% the best of the previous study (Huynh et al. 2021) in terms of the Top 1 accuracy, which was presented in the conference IEA/AIE 2021.

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Notes

  1. https://fasttext.cc/docs/en/english-vectors.html

  2. https://gist.github.com/sebleier/554280

  3. https://www.springer.com/gp

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Acknowledgements

Son Huynh Thanh was funded by Vingroup JSC and supported by the Master, PhD Scholarship Programme of Vingroup Innovation Foundation (VINIF), Institute of Big Data, code VINIF.2021.ThS.18

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Authors and Affiliations

  1. AISIA Research Lab, Ho Chi Minh City, Vietnam

    Son T. Huynh, Nhi Dang, Dac H. Nguyen, Phong T. Huynh & Binh T. Nguyen

  2. Vietnam National University, Ho Chi Minh City, Vietnam

    Son T. Huynh, Nhi Dang & Binh T. Nguyen

  3. University of Science, Ho Chi Minh City, Vietnam

    Son T. Huynh, Nhi Dang & Binh T. Nguyen

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  1. Son T. Huynh
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  2. Nhi Dang
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  3. Dac H. Nguyen
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  4. Phong T. Huynh
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Correspondence to Binh T. Nguyen.

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This article belongs to the Topical Collection: Emerging Topics in Artificial Intelligence Selected from IEA/AIE2021

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Huynh, S.T., Dang, N., Nguyen, D.H. et al. FPSRS: a fusion approach for paper submission recommendation system. Appl Intell 53, 8614–8630 (2023). https://doi.org/10.1007/s10489-022-04117-8

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