A Novel Article Recommendation System Empowered by the Hybrid Combinations of Content-Based State-of-the-Art Methods

Yıl 2023, Cilt: 11 Sayı: 1, 1 - 12, 31.03.2023

İlya Kuş Sinem Bozkurt Keser Savaş Okyay

https://doi.org/10.18100/ijamec.1199886

Öz

The initial literature reviewing step is of great importance during any scientific reporting. Nevertheless, finding relevant papers grows tough as the number of online scientific publications rapidly increases. Correspondingly, the need for article recommendation systems has emerged, which aim to recommend new papers suitable for the researchers’ interests. Using these systems provides researchers access to related publications quickly and effectively. In this study, a novel article recommendation system, which is empowered by the hybrid combinations of content-based state-of-the-art methods, is proposed. Various methods are utilized comparatively for an in-depth analysis, and user profiles are evaluated. 41,000 articles collected from the ARXIV dataset are used in the performance evaluation. In the experiments in which Word2vec and LDA are combined, Precision@50, Recall@50, and F1-score@50 achieve the highest performance with .206, .791, and .498 values, respectively. The in-depth analysis and the numerical findings justify that the proposed system is strong and promising compared to the literature.

Anahtar Kelimeler

Content-based filtering, Latent dirichlet allocation, Recommender system, Word embedding algorithm

Kaynakça

• Reddy, S. R. S., Nalluri, S., Kunisetti, S., Ashok, S., & Venkatesh, B. Content-based movie recommendation system using genre correlation. Singapore, In Smart Intelligent Computing and Applications, 2019, p. 391-397.
• Qi, T., Wu, F., Wu, C., & Huang, Y. Personalized news recommendation with knowledge-aware interactive matching. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021. p. 61-70.
• Schedl, M. Deep learning in music recommendation systems. Frontiers in Applied Mathematics and Statistics, 2019. p. 44.
• Devika, P., Jisha, R. C., & Sajeev, G. P. A novel approach for book recommendation systems. In 2016 IEEE international conference on computational intelligence and computing research (ICCIC), 2016. p. 1-6
• Gopalakrishnan, T., Sengottuvelan, P., Bharathi, A., & Lokeshkumar, R. An approach to webpage prediction method using variable order Markov model in recommendation systems. Journal of Internet Technology, 2018. 19(2): p. 415-424.
• Bai, X., Wang, M., Lee, I., Yang, Z., Kong, X., & Xia, F. Scientific paper recommendation: A survey. IEEE Access, 2019. 7: p. 9324-9339.
• Das, J., Majumder, S., Dutta, D., & Gupta, P. Iterative use of weighted voronoi diagrams to improve scalability in recommender systems. In Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2015. p. 605-617.
• Lu, J., Wu, D., Mao, M., Wang, W., & Zhang, G. Recommender system application developments: a survey. Decision Support Systems, 2015. 74: p. 12-32.
• Dhanda, M., & Verma, V. Recommender system for academic literature with incremental dataset. Procedia Computer Science, 2016. 89: p. 483-491.
• Al Alshaikh, M., Uchyigit, G., & Evans, R. A research paper recommender system using a Dynamic Normalized Tree of Concepts model for user modelling. In 2017 11th International Conference on Research Challenges in Information Science (RCIS), 2017. p. 200-210.
• Hassan, H. A. M. Personalized research paper recommendation using deep learning. In Proceedings of the 25th conference on user modeling, adaptation and personalization, 2017. p. 327-330.
• Bhagavatula, C., Feldman, S., Power, R., & Ammar, W. Content-based citation recommendation. In Proc. of NAACL, 2018.
• Wang, D., Liang, Y., Xu, D., Feng, X., & Guan, R. A content-based recommender system for computer science publications. Knowledge-Based Systems, 2018. p. 157, 1-9.
• Kim, S. W., & Gil, J. M. Research paper classification systems based on TF-IDF and LDA schemes. Human-centric Computing and Information Sciences, 2019. 9(1):p. 1-21.
• Jain, S., Khangarot, H., & Singh, S. Journal recommendation system using content-based filtering. In Recent developments in machine learning and data analytics, 2019. p. 99-108.
• Olshannikova, E., Olsson, T., Huhtamäki, J., & Yao, P. Scholars’ Perceptions of Relevance in Bibliography-Based People Recommender System. Computer Supported Cooperative Work (CSCW), 2019. 28(3):p. 357-389.
• Haruna, K., Ismail, M. A., Qazi, A., Kakudi, H. A., Hassan, M., Muaz, S. A., & Chiroma, H. Research paper recommender system based on public contextual metadata. Scientometrics, 2020. 125(1):p. 101-114.
• Pradhan, T., & Pal, S. CNAVER: A content and network-based academic venue recommender system. Knowledge-Based Systems, 2020. 189: p. 105092.
• Öz, V. K., Deniz, E., Keser, S. B., Kartal, Y., & Okyay, S. Yeni Bir İçerik-Tabanlı Akademik Makale Tavsiye Sistemi Prototipi Geliştirilmesi. Eskişehir Türk Dünyası Uygulama ve Araştırma Merkezi Bilişim Dergisi, 2021. 2(2): p. 6-11.
• Bagul, D. V., & Barve, S. A novel content-based recommendation approach based on LDA topic modeling for literature recommendation. In 2021 6th International Conference on Inventive Computation Technologies (ICICT), 2021 p. 954-961.
• Deniz, E., ÖZ, V. K., Keser, S. B., Okyay, S., & Kartal, Y. İçerik tabanlı bilimsel yayın öneri sisteminde benzerlik ölçümlerinin incelenmesi. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 2021, 12(2): p. 221-228.
• ZhengWei, H., JinTao, M., YanNi, Y., Jin, H., & Ye, T. Recommendation method for academic journal submission based on doc2vec and XGBoost. Scientometrics, 127(5): p. 2381-2394.
• Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems, 2013. p. 26.
• Le, Q., & Mikolov, T. Distributed representations of sentences and documents. In International conference on machine learning, 2014 p. 1188-1196.
• Morsomme, R., & Alferez, S. V. Content-Based Course Recommender System for Liberal Arts Education. International Educational Data Mining Society, 2019.
• Nandi, R. N., Zaman, M. A., Al Muntasir, T., Sumit, S. H., Sourov, T., & Rahman, M. J. U. Bangla news recommendation using doc2vec. In 2018 International Conference on Bangla Speech and Language Processing (ICBSLP), 2018. p. 1-5.
• Blei, D. M., Ng, A. Y., & Jordan, M. I. Latent dirichlet allocation. Journal of machine Learning research, 2003. p. 993-1022.
• Sakib, N., Ahmad, R. B., & Haruna, K. A collaborative approach toward scientific paper recommendation using citation context, 2020. 8: p. 51246-51255.
• Gündoğan, E., & Kaya, M. Research paper classification based on Word2vec and community discovery. In 2020 International Conference on Decision Aid Sciences and Application (DASA), 2020. p. 1032-1036.
• Patra, B. G., Maroufy, V., Soltanalizadeh, B., Deng, N., Zheng, W. J., Roberts, K., & Wu, H. A content-based literature recommendation system for datasets to improve data reusability–a case study on gene expression omnibus (geo) datasets. Journal of Biomedical Informatics, 2020. p.104, 103399.
• Kanakia, A., Shen, Z., Eide, D., & Wang, K. A scalable hybrid research paper recommender system for microsoft academic. In The world wide web conference. 2019. p. 2893-2899.
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