Abstract
Academic articles recommendation systems have gained a lot of interest as an effective tool to suggest relevant articles for researchers according to their interests. Explicit identification of the topics of interests from the contents of academic articles that the researchers have authored, downloaded or read has been always a challenging task. In this paper, we propose a concept-based method to represent researchers’ interests where the concepts generation process depends on the semantics of the words in the articles related to the researcher. The evaluation results show that the proposed method outperforms the recommendation baseline methods and produces better recommendations for researchers.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wang, J., Song, H., Zhou, X.: A collaborative filtering recommendation algorithm based on biclustering. In: 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), Chengdu, pp. 803–807. IEEE (2015). https://doi.org/10.1109/CYBER.2015.7288046
Asanov, D., et al.: Algorithms and Methods in Recommender Systems. Berlin Institute of Technology, Berlin (2011)
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)
Beel, J., Gipp, B., Langer, S., Breitinger, C.: Research-paper recommender systems: a literature survey. Int. J. Digit. Libr. 17, 305–338 (2016)
Ramos, J., et al.: Using TF-IDF to determine word relevance in document queries. In: Proceedings of the First Instructional Conference on Machine Learning, pp. 133–142 (2003)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv Preprint arXiv:1301.3781 (2013)
Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: International Conference on Machine Learning, pp. 1188–1196 (2014)
Manning, C.D., Raghavan, P., Schütze, H.: Scoring, term weighting and the vector space model. In: Introduction to Information Retrieval, vol. 100, pp. 2–4. Cambridge University Press, Cambridge (2008)
Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009, 1–19 (2009)
Wu, C.-S.M., Garg, D., Bhandary, U.: Movie recommendation system using collaborative filtering. In: 2018 IEEE 9th International Conference on Software Engineering and Service Science (ICSESS), pp. 11–15 (2018)
Shakirova, E.: Collaborative filtering for music recommender system. In: 2017 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), pp. 548–550 (2017)
Pazzani, M.J., Billsus, D.: Content-based recommendation systems. In: The Adaptive Web, pp. 325–341. Springer (2007)
Tewari, A.S., Barman, A.G.: Collaborative book recommendation system. In: 2016 2nd International Conference on Contemporary Computing and Informatics, pp. 85–88 (2016). https://doi.org/10.1109/IC3I.2016.7917939
Kompan, M., Bieliková, M.: Content-based news recommendation. In: International Conference on Electronic Commerce and Web Technologies, pp. 61–72 (2010)
Philip, S., Shola, P., Ovye, A.: Application of content-based approach in research paper recommendation system for a digital library. Int. J. Adv. Comput. Sci. Appl. 5, 37–40 (2014)
Chang, T.-M., Hsiao, W.-F.: LDA-based personalized document recommendation. In: PACIS, p. 13 (2013)
Wang, C., Blei, D.M.: Collaborative topic modeling for recommending scientific articles. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 448–456 (2011)
Amami, M., Pasi, G., Stella, F., Faiz, R.: An LDA-based approach to scientific paper recommendation. In: International Conference on Applications of Natural Language to Information Systems, pp. 200–210 (2016)
Jiang, Y., Jia, A., Feng, Y., Zhao, D.: Recommending academic papers via users’ reading purposes. In: Proceedings of the Sixth ACM Conference on Recommender Systems, pp. 241–244 (2012)
Nandi, R.N., Zaman, M.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), pp. 1–5 (2018)
Phi, V.-T., Chen, L., Hirate, Y.: Distributed representation based recommender systems in e-commerce. In: DEIM Forum (2016)
Lloyd, S.: Least squares quantization in PCM. IEEE Trans. Inf. Theory 28, 129–137 (1982)
MacQueen, J., et al.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297 (1967)
Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Inf. Process. Manage. 24, 513–523 (1988)
Powers, D.M.W.: Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. J. Mach. Learn. Technol. 2, 37–63 (2011)
Li, Y., Yang, M., Zhang, Z.M.: Scientific articles recommendation. In: Proceedings of the 22nd ACM International Conference on Conference on Information & Knowledge Management, pp. 1147–1156 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Mohamed, D., El-Kilany, A., Mokhtar, H.M.O. (2021). Academic Articles Recommendation Using Concept-Based Representation. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2020. Advances in Intelligent Systems and Computing, vol 1251. Springer, Cham. https://doi.org/10.1007/978-3-030-55187-2_52
Download citation
DOI: https://doi.org/10.1007/978-3-030-55187-2_52
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-55186-5
Online ISBN: 978-3-030-55187-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)