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A novel hybrid paper recommendation system using deep learning

Author

Listed:
  • Esra Gündoğan

    (Fırat University)

  • Mehmet Kaya

    (Fırat University)

Abstract

Every year, thousands of papers are published in journals and conferences by researchers in many different fields. These papers are an important guide for other researchers. However, the increasing amount of digital data with the development of information technologies makes it difficult to reach the desired information. Recommendation systems play an important role in facilitating researchers' access to studies on their subjects. It provides faster and easier access to papers on the desired subject. Recommendation systems are developed according to the user profile or subject. In this paper, a novel hybrid paper recommendation system based on deep learning is proposed. The method uses a combination of document similarity, hierarchical clustering, and keyword extraction. Our aim is to group papers in different fields such as computer science, economics, medicine, or in a specific field, according to their subjects, and to present papers with high semantic similarity to the user according to the query entered. The study has been applied on real dataset containing papers from different categories such as machine learning, artificial intelligence, human–computer interaction in computer science. The success of each stage of the study has been evaluated separately. However, looking at the system as a whole, the overall performance of the proposed approach is 80%. Papers having high similarity with their queries have been recommended to users. Thus, access to the studies on the desired subject in the huge amount of papers has been made faster and easier.

Suggested Citation

  • Esra Gündoğan & Mehmet Kaya, 2022. "A novel hybrid paper recommendation system using deep learning," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(7), pages 3837-3855, July.
    • Handle: RePEc:spr:scient:v:127:y:2022:i:7:d:10.1007_s11192-022-04420-8
    DOI: 10.1007/s11192-022-04420-8
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    References listed on IDEAS

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    1. Hongbin Wang & Jingzhen Ye & Zhengtao Yu & Jian Wang & Cunli Mao, 2020. "Unsupervised Keyword Extraction Methods Based on a Word Graph Network," International Journal of Ambient Computing and Intelligence (IJACI), IGI Global, vol. 11(2), pages 68-79, April.
    2. Hanwen Liu & Huaizhen Kou & Chao Yan & Lianyong Qi, 2020. "Keywords-Driven and Popularity-Aware Paper Recommendation Based on Undirected Paper Citation Graph," Complexity, Hindawi, vol. 2020, pages 1-15, April.
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    Cited by:

    1. Mohammed Azmi Al-Betar & Ammar Kamal Abasi & Ghazi Al-Naymat & Kamran Arshad & Sharif Naser Makhadmeh, 2023. "Optimization of scientific publications clustering with ensemble approach for topic extraction," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(5), pages 2819-2877, May.
    2. Xiaojuan Zhang & Shuqi Song & Yuping Xiong, 2024. "Personalized global citation recommendation with diversification awareness," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(7), pages 3625-3657, July.
    3. Chi Jiang & Xiao Ma & Jiangfeng Zeng & Yin Zhang & Tingting Yang & Qiumiao Deng, 2023. "TAPRec: time-aware paper recommendation via the modeling of researchers’ dynamic preferences," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(6), pages 3453-3471, June.

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    2. Tingting Zhang & Baozhen Lee & Qinghua Zhu & Xi Han & Ke Chen, 2023. "Document keyword extraction based on semantic hierarchical graph model," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(5), pages 2623-2647, May.

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