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Content Based Scientific Article Recommendation System Using Deep Learning Technique

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Inventive Systems and Control

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 204))


The emergence of the era of big data has increased the ease with which scientific users can access academic articles with better efficiency and accuracy from a pool of papers available. With the exponential increase in the number of research papers that are getting published every year, it has made scholars face the problem of information overload where they find it difficult to conduct comprehensive literature surveys. An article recommendation system helps in overcoming this issue by providing users with personalized recommendations based on their interests and choices. The common approaches used for recommendation are Content-Based Filtering (CBF) and Collaborative Filtering (CF). Even though there is much advancement in the field of article recommendation systems, a content-based approach using a deep learning technology is still in its inception. In this work, a C-SAR model using Gated Recurrent Unit (GRU) and association rule mining Apriori algorithm to provide a recommendation of articles based on the similarity in the content were proposed. The combination of a deep learning technique along with a classical algorithm in data mining is expected to provide better results than the state-of-art model in suggesting similar papers.

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  1. X. Bai, M. Wang, I. Lee, Z. Yang, X. Kong, F. Xia, Scientific Paper Recommendation: A Survey. IEEE Access 7, 9324–9339 (2019)

    Article  Google Scholar 

  2. M. Asim and S. Khusro, “Content Based Call for Papers Recommendation to Researchers”, 12th International Conference on Open Source Systems and Technologies Lahore, Pakistan, 2018, pp. 42–47

    Google Scholar 

  3. C. Bhagavatula, S. Feldman, R. Power and W. Ammar,” Content-Based Citation Recommendation”, Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, New Orleans, Louisiana, Vol.1, Jun 2018

    Google Scholar 

  4. B. Kazemi and A. Abhari,” A Comparative Study on Content-Based Paper-To-Paper Recommendation Approaches In Scientific Literature”, SpringSim-CNS, Apr 2017, pp. 23–26

    Google Scholar 

  5. S. Philip, P.B. Shola and A.O. John, “Application of Content-Based Approach in Research Paper Recommendation System for a Digital Library” International Journal of Advanced Computer Science and Applications, 2014

    Google Scholar 

  6. D. Hanyurwimfura, L. Bo, V. Havyarimana, D. Njagi and F. Kagorora,” An Effective Academic Research Papers Recommendation for Non-profiled Users”, International Journal of Hybrid Information Technology, Vol. 8, 2015, pp. 255–272

    Google Scholar 

  7. A. Samad, M. A. Islam, M. A. Iqbal and M. Aleem,” Centrality-Based Paper Citation Recommender System”, EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, Jun 2019

    Google Scholar 

  8. L. Guo, X. Cai, H. Qin, Y. Guo, F. Li and G. Tian,” Citation Recommendation with a Content-Sensitive DeepWalk based Approach”, International Conference on Data Mining Workshops, Beijing, China, 2019, pp. 538–543

    Google Scholar 

  9. H. A. M. Hassan,” Personalized Research Paper Recommendation using Deep Learning”, Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, Jul 2017, pp. 327–330

    Google Scholar 

  10. K. Hong, H. Jeon and C. Jeon,” Advanced Personalized Research Paper Recommendation System Based on Expanded User Profile through Semantic Analysis”, International Journal of Digital Content Technology and its Applications, 2013, pp. 67–76

    Google Scholar 

  11. A. Suglia, C. Greco, C. Musto, M. Gemmis, P. Lops and G. Semeraro,”A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks”, Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, Jul 2017, pp. 202–211

    Google Scholar 

  12. W. Huang, Z. Wu, C. Liang, P. Mitra, and C.L. Giles,” A Neural Probabilistic Model for Context Based Citation Recommendation”, AAAI, 2015

    Google Scholar 

  13. Z. Li and X. Zou,” A Review on Personalized Academic Paper Recommendation”, Computer and Information Science, 2019

    Google Scholar 

  14. R. Sharma, D. Gopalani and Y. Meena, “Concept-Based Approach for Research Paper Recommendation” PReMI, 2017

    Google Scholar 

  15. M. A. Arif, “Content aware citation recommendation system,” International Conference on Emerging Technological Trends, Kollam, 2016, pp. 1–6

    Google Scholar 

  16. J. Shu, X. Shen, H. Liu, B. Yi and Z. Zhang,” A content-based recommendation algorithm for learning resources”, Multimedia Systems, 2017

    Google Scholar 

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Correspondence to Akhil M. Nair .

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Nair, A.M., Benny, O., George, J. (2021). Content Based Scientific Article Recommendation System Using Deep Learning Technique. In: Suma, V., Chen, J.IZ., Baig, Z., Wang, H. (eds) Inventive Systems and Control. Lecture Notes in Networks and Systems, vol 204. Springer, Singapore.

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