Document Type : Original/Review Paper

Authors

Department of Computer Engineering, University of Guilan, Rasht, Iran.

Abstract

Online scientific communities are bases that publish books, journals, and scientific papers, and help promote knowledge. The researchers use search engines to find the given information including scientific papers, an expert to collaborate with, and the publication venue, but in many cases due to search by keywords and lack of attention to the content, they do not achieve the desired results at the early stages. Online scientific communities can increase the system efficiency to respond to their users utilizing a customized search. In this paper, using a dataset including bibliographic information of user’s publication, the publication venues, and other published papers provided as a way to find an expert in a particular context where experts are recommended to a user according to his records and preferences. In this way, a user request to find an expert is presented with keywords that represent a certain expertise and the system output will be a certain number of ranked suggestions for a specific user. Each suggestion is the name of an expert who has been identified appropriate to collaborate with the user. In evaluation using IEEE database, the proposed method reached an accuracy of 71.50 percent that seems to be an acceptable result.

Keywords

[1] Sathiyabama, M. T., & Vivekanandan, K. (2011). Personalized Web Search Techniques-A Review. Global Journal of Computer Science and Technology.
[2] Dadiyala, C., Patil, P., & Agrawal, G. (2013). Personalized web search. Int. J. Adv. Res. Comput. Sci. Softw. Eng, vol. 3, no. 6.
[3] Afzal, M. T., & Maurer, H. A. (2011). Expertise Recommender System for Scientific Community. J. UCS, vol. 17, no. 11, pp. 1529-1549.
[4] Zhan, Z., Yang, L., Bao, S., Han, D., Su, Z., & Yu, Y. (2011). Finding appropriate experts for collaboration. In International Conference on Web-Age Information Management. Springer, Berlin, Heidelberg.
[5] Davoodi, E., Kianmehr, K., & Afsharchi, M. (2013). A semantic social network-based expert recommender system. Applied Intelligence, vol. 39, no. 1, pp. 1-13.
[6] Husain, O., Salim, N., Alias, R. A., Abdelsalam, S., & Hassan, A. (2019). Expert Finding Systems: A Systematic Review. Applied Sciences, vol. 9, no. 20, pp. 4218-4250.
[7] Xia, F., Wang, W., Bekele, T. M., & Liu, H. (2017). Big scholarly data: A survey. IEEE Transactions on Big Data, vol. 3, no. 1, pp. 18-35.
[8] Ricci, F., Rokach, L., & Shapira, B. (2011). Introduction to recommender systems handbook. In Recommender systems handbook. Springer US.
[9] Zanker, M., Felfernig, A., & Friedrich, G. (2011). Recommender systems: an introduction.
[10] Safa, R., Mirroshandel, S., Javadi, S., & Azizi, M. (2018). Venue Recommendation Based on Paper’s Title and Co-authors Network. Journal of Information Systems and Telecommunication, vol. 6, no. 1, pp. 33-40.
[11] Yukawa, T., Kasahara, K., Kato, T., & Kita, T. (2001). An expert recommendation system using concept-based relevance discernment. In Tools with Artificial Intelligence, Proceedings of the 13th International Conference on (pp. 257-264). IEEE.
[12] Rohini, U., & Ambati, V. (2005). A collaborative filtering based re-ranking strategy for search in digital libraries. In International Conference on Asian Digital Libraries (pp. 194-203). Springer, Berlin, Heidelberg.
[13] Yang, C., Ma, J., Silva, T., Liu, X., & Hua, Z. (2013). A multilevel information mining approach for expert recommendation in online scientific communities. The Computer Journal, vol. 58, no. 9, pp. 1921-1936.
[14] Sun, Y., Barber, R., Gupta, M., Aggarwal, C. C., & Han, J. (2011). Co-author relationship prediction in heterogeneous bibliographic networks. In Advances in Social Networks Analysis and Mining (ASONAM), 2011 International Conference on (pp. 121-128). IEEE.
[15] Lee, D. H., Brusilovsky, P., & Schleyer, T. (2011). Recommending collaborators using social features and mesh terms. Proceedings of the Association for Information Science and Technology, vol. 48, no. 1, pp. 1-10.
[16] Middleton, S. E., Shadbolt, N. R., & De Roure, D. C. (2004). Ontological user profiling in recommender systems. ACM Transactions on Information Systems (TOIS), vol. 22, no. 1, pp. 54-88.
[17] Liu, X., Bollen, J., Nelson, M. L., & Van de Sompel, H. (2005). Co-authorship networks in the digital library research community. Information processing & management, vol. 41, no. 6, pp. 1462-1480.
[18] Han, S., He, D., Brusilovsky, P., & Yue, Z. (2013). Coauthor prediction for junior researchers. In International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction (pp. 274-283). Springer, Berlin, Heidelberg.
[19] Yang, C., Sun, J., Ma, J., Zhang, S., Wang, G., & Hua, Z. (2015). Scientific collaborator recommendation in heterogeneous bibliographic networks. In System Sciences (HICSS), 2015 48th Hawaii International Conference on (pp. 552-561). IEEE.
[20] Hu, D., & Zhao, J. L. (2008). Expert recommendation via semantic social networks. ICIS 2008 Proceedings.
[21] Fazel-Zarandi, M., Devlin, H. J., Huang, Y., & Contractor, N. (2011). Expert recommendation based on social drivers, social network analysis, and semantic data representation. In Proceedings of the 2nd international workshop on information heterogeneity and fusion in recommender systems (pp. 41-48). ACM.
[22] Wang, G. A., Jiao, J., Abrahams, A. S., Fan, W., & Zhang, Z. (2013). ExpertRank: A topic-aware expert finding algorithm for online knowledge communities. Decision Support Systems, vol. 54, no. 3, pp. 1442-1451.
[23] Nikzad–Khasmakhi, N., Balafar, M. A., & Feizi–Derakhshi, M. R. (2019). The state-of-the-art in expert recommendation systems. Engineering Applications of Artificial Intelligence, vol. 82, no. 1, pp. 126-147.
[24] Amato, F., Cozzolino, G., & Sperlì, G. (2019). A hypergraph data model for expert-finding in multimedia social networks. Information, vol. 10, no. 6, pp. 183-193.
[25] Ley, M. (2009). DBLP: some lessons learned. Proceedings of the VLDB Endowment, vol. 2, no. 2, pp. 1493-1500.
[26] IEEE Xplore (2020), Available: https://ieeexplore.ieee.org/Xplore/home.jsp
[27] IEEE Xplore API Portal (2020), Available: https://developer.ieee.org.
[28] Tahmasebi, M., Fotouhi, F., & Esmaeili, M. (2019). Hybrid Adaptive Educational Hypermedia‎ Recommender Accommodating User’s Learning‎ Style and Web Page Features‎. Journal of AI and Data Mining, vol. 7, no. 2, pp. 225-238.