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User’s Research Interests Based Paper Recommendation System: A Deep Learning Approach

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Putting Social Media and Networking Data in Practice for Education, Planning, Prediction and Recommendation

Part of the book series: Lecture Notes in Social Networks ((LNSN))

Abstract

Researchers and scientists read articles to improve their studies. Researchers spend a lot of time trying to find the suitable article from among a large number of publications. The purpose of the article recommendation system is to shorten the working time and to present the research articles to the researchers. Classic article recommendation systems do not consider the user’s information. They show the same results in the same sort for each researcher.

This chapter is based on the user’s profile and aims to recommend articles that are most relevant to the user’s search. In this study, an article recommendation system, that takes into consideration the researcher’s previous articles and the study field, is presented. One of the most important innovations of this study is the use of TF-IDF and Cosine similarity to recommend article taking user’s past articles into consideration. As a result of the study, the proposed method has achieved more successful results compared to equivalent methods according to F-Measure criterion. Our study was repeated with Doc2vec method which is one of the Deep-Learning methods. It was obtained better results than TF-IDF and Cosine methods.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Fırat University, Elazığ, Turkey

    Betül Bulut, Esra Gündoğan & Mehmet Kaya

  2. Department of Electronics and Automation, Fırat University, Elazığ, Turkey

    Buket Kaya

  3. Department of Computer Science, University of Calgary, Calgary, AB, Canada

    Reda Alhajj

  4. Department of Computer Engineering, Istanbul Medipol University, Istanbul, Turkey

    Reda Alhajj

Authors
  1. Betül Bulut
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  2. Esra Gündoğan
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  3. Buket Kaya
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  4. Reda Alhajj
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  5. Mehmet Kaya
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Corresponding author

Correspondence to Mehmet Kaya .

Editor information

Editors and Affiliations

  1. Computer Engineering, Fırat University, Elazığ, Turkey

    Mehmet Kaya

  2. Ministry of Health, Ankara, Turkey

    Şuayip Birinci

  3. Department of Computer Science, University of Calgary, Calgary, AB, Canada

    Jalal Kawash

  4. Department of Computer Science, University of Calgary, Calgary, AB, Canada

    Reda Alhajj

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Bulut, B., Gündoğan, E., Kaya, B., Alhajj, R., Kaya, M. (2020). User’s Research Interests Based Paper Recommendation System: A Deep Learning Approach. In: Kaya, M., Birinci, Ş., Kawash, J., Alhajj, R. (eds) Putting Social Media and Networking Data in Practice for Education, Planning, Prediction and Recommendation. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-030-33698-1_7

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