User Profile Based Paper Recommendation System

With the spread of science and the increasing number of researchers working in academic fields, there has also been a significant increase in the number of academic publications. Researchers always follow new works published for keeping their knowledge up to date. However, due to thousands of academic publications published every day from many academic sources, academics are not always able to find publications about their subjects. Today, almost all of online academic databases employ a recommendation module that only considers the studies similar to the paper that the user looked at. However, a recommendation system based on the information of a single article is often not enough. In this study, the proposed method recommends by considering user's publications, user’s co-authors and co-authors’ papers. Therefore, meta- data of the articles published by the researcher in the past are evaluated as time-awareness by the method we proposed. In this way, the most relevant articles to the user's profile can be found by using the proposed method in the data repository created from the exact contents of hundreds of thousands of academic works. The method uses TF-IDF frequency-based similarity analysis method. In the evaluation phase, the performance of the proposed method was examined. The accuracy of the method was measured by several different tests. The results are very promising and demonstrate that the method can produce accurate and quality results.

PDF

___

[1] W. Choochaiwattana, “Usage of tagging for research paper recommendation,” in ICACTE 2010 - 2010 3rd International Conference on Advanced Computer Theory and Engineering, Proceedings, 2010, vol. 2.

[2] S. Bahulikar, “Analyzing recommender systems and applying a location based approach using tagging,” in 2017 2nd International Conference for Convergence in Technology (I2CT), 2017.

[3] R. K. Mysore, M. Junichiro, and S. Ichiro, “Cross-Domain Academic Paper Recommendation by Semantic Linkage Approach Using Text Analysis and Recurrent Neural Networks,” in Management of Engineering and Technology (PICMET), 2017 Portland International Conference on, 2017.

[4] S. S. Weng and H. L. Chang, “Using ontology network analysis for research document recommendation,” Expert Syst. Appl., vol. 34, no. 3, pp. 1857–1869, 2008.

[5] K. V. Neethukrishnan and K. P. Swaraj, “Ontology based research paper recommendation using personal ontology similarity method,” in Electrical, Computer and Communication Technologies (ICECCT), 2017 Second International Conference on, 2017.

[6] Y. C. Lee et al., “Recommendation of research papers in DBpia: A Hybrid approach exploiting content and collaborative data,” in 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings, 2017, pp. 2966–2971.

[7] C. Bancu, M. Dagadita, M. Dascalu, C. Dobre, S. Trausan-Matu, and A. M. Florea, “ARSYS-article recommender system,” in Proceedings - 14th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2012, 2012, pp. 349–355.

[8] W. Zhao, R. Wu, and H. Liu, “Paper recommendation based on the knowledge gap between a researcher’s background knowledge and research target,” Inf. Process. Manag., vol. 52, no. 5, pp. 976–988, 2016.

[9] J. D. West, I. Wesley-Smith, and C. T. Bergstrom, “A Recommendation System Based on Hierarchical Clustering of an Article-Level Citation Network,” IEEE Trans. Big Data, vol. 2, no. 2, pp. 113–123, 2016.

[10] F. Xia, H. Liu, I. Lee, and L. Cao, “Scientific Article Recommendation: Exploiting Common Author Relations and Historical Preferences,” IEEE Trans. Big Data, vol. 2, no. 2, pp. 101– 112, 2016.

[11] Q. Zhou, X. Chen, and C. Chen, “Authoritative scholarly paper recommendation based on paper communities,” in Proceedings - 17th IEEE International Conference on Computational Science and Engineering, CSE 2014, Jointly with 13th IEEE International Conference on Ubiquitous Computing and Communications, IUCC 2014, 13th International Symposium on Pervasive Systems, , 2015, pp. 1536–1540.

[12] C. H. Chen, S. D. Mayanglambam, F. Y. Hsu, C. Y. Lu, H. M. Lee, and J. M. Ho, “Novelty paper recommendation using citation authority diffusion,” in Proceedings - 2011 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2011, 2011, pp. 126–131.

[13] W. Zhang, T. Yoshida, and X. Tang, “A comparative study of TF*IDF, LSI and multi-words for text classification,” Expert Syst. Appl., vol. 38, no. 3, pp. 2758–2765, 2011.

[14] J. Ramos, “Using TF-IDF to Determine Word Relevance in Document Queries,” Proc. first Instr. Conf. Mach. Learn., pp. 1–4, 2003.

[15] T. Kenter and M. de Rijke, “Short Text Similarity with Word Embeddings Categories and Subject Descriptors,” Proc. 24th ACM Int. Conf. Inf. Knowl. Manag. (CIKM 2015), pp. 1411–1420, 2015.

[16] S. Albitar, S. Fournier, and B. Espinasse, “An Effective TF/IDF-Based Text-to-Text Semantic Similarity Measure for Text Classification,” Web Inf. Syst. Eng. - Wise 2014, Pt I, vol. 8786, pp. 105–114, 2014.

[17] C.-H. HUANG, J. YIN, and F. HOU, “A text similarity measurement combining word semantic information with TF-IDF method,” in Chinese Journal of Computers, 2011.

[18] S. H. Strogatz, “Exploring complex networks.,” Nature, vol. 410, no. 6825, pp. 268–276, 2001.

[19] M. E. J. Newman, “The Structure and Function of Complex Networks,” SIAM Review, vol. 45, no. 2. pp. 167–256, 2003.

[20] M. Faloutsos, P. Faloutsos, and C. Faloutsos, “On Power-Law Relationships of the Internet Topology,” in In SIGCOMM, 1999, pp. 251–262.

[21] R. Albert and A. L. Barabasi, “Statistical mechanics of complex networks,” Rev. Mod. Phys., vol. 74, no. 1, pp. 47–97, 2002.

[22] D. Krackhardt, “The strength of strong ties: The importance of Philos in organizations,” Networks Organ. Struct. Form, Action, vol. 216, pp. 216–239, 1992.

[23] M. S. Granovetter, “The Strength of Weak Ties,” American Journal of Sociology, vol. 78, no. 6. pp. 1360–1380, 1973.

[24] A. A. Müngen and M. Kaya, “A Novel Method for Event Recommendation in Meetup,” in 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2017.

[25] E. Gündoğan, B. Kaya, and M. Kaya, “Prediction of Symptom-Disease Links in Online Helath Forums,” in IEEE/ACM International Conference on Advances in Social Networks and Mining (ASONAM, 2017), 2017.

[26] S. Aslan and M. Kaya, “Link prediction methods in bipartite networks,” in Computer Science and Engineering (UBMK), 2017 International Conference on, 2017.

[27] M. Ohta, T. Hachiki, and A. Takasu, “Related paper recommendation to support online-browsing of research papers,” in 4th International Conference on the Applications of Digital Information and Web Technologies, ICADIWT 2011, 2011, pp. 130–136.

[28] H. Jeon and C. Jeon, “UserProfile-Based Personalized Research Paper Recommendation System,” Comput. Netw. Technol. (ICCNT), 2012 8th Int. Conf., pp. 134–138, 2012.

[29] H. Xue, J. Guo, Y. Lan, and L. Cao, “Personalized paper recommendation in online social scholar system,” in ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2014, pp. 612–619.

[30] J. Chen and Z. Ban, “Literature recommendation by researchers’ publication analysis,” in Information and Automation (ICIA), 2016 IEEE International Conference on, 2016.

[31] H. A. M. Hassan, “Personalized Research Paper Recommendation using Deep Learning,” in Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization - UMAP ’17, 2017, pp. 327–330.

[32] T. Achakulvisut, DE Acuna, T. Ruangrong, K. Kording, “Science Concierge: A fast content-based recommendation system for scientific publications”, PloS one 11 (7), e0158423, 2016