Combining Clustering and Regression Models for Recommending Researchers
Resumo
Due to the increase in scientific production, especially in recent years, management and decision support challenge also increase significantly. The task of recommending researchers, for example, to a project is not simple. Even with the proper amount of data, ranking and recommending researchers becomes a challenging process. Despite the different methods, what can happen is that the datasets of an institution or research areas do not have a ranking value, that is, a value that can be used to assess the position of a researcher. Even having a necessary dataset, there is no ranking information for these researchers, and this process of obtaining data for training a model can be costly. We propose to use clustering techniques to support the ranking process, reducing the human effort to obtain examples for models training. Then, we used this dataset to train the regression models and Mean Squared Error (MSE) and Normalized Discounted Cumulative Gain (nDCG) to evaluate them. Tests demonstrate that our solution can support the researchers' recommendation process in an adaptive process to the needs of an organization.
Palavras-chave:
recommender systems, academic analytics, machine learning, support vector machine, linear regression
Referências
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Tatiya, R. V. and Vaidya, A. S. A survey of recommendation algorithms. IOSR J. Comput. Eng 16 (6): 1619, 2014.
Wang, G., He, X., and Ishuga, C. I. HARSI: A novel hybrid article recommendation approach integrating with social information in scientific social network. KnowledgeBased Systems vol. 148, pp. 8599, 2018. Publisher: Elsevier.
Yang, Z., Yin, D., and Davison, B. D. Recommendation in academia: A joint multirelational model. IEEE, pp. 566571, 2014.
Yu, S., Liu, J., Yang, Z., Chen, Z., Jiang, H., Tolba, A., and Xia, F. PAVE: Personalized Academic Venue recommendation Exploiting copublication networks. Journal of Network and Computer Applications vol. 104, pp. 3847, 2018.
Zriaa, R. and Amali, S. A Comparative Study Between KNearest Neighbors and KMeans Clustering Techniques of Collaborative Filtering in eLearning Environment. Lecture Notes in Networks and Systems vol. 183, pp. 268282, 2021.
Beel, J., Gipp, B., Langer, S., and Breitinger, C. paper recommender systems: A literature survey. International Journal on Digital Libraries 17 (4): 305338, 2016. Publisher: Springer.
Bidgoli, A., Rahnamayan, S., Mahdavi, S., and Deb, K. A Novel ParetoVIKOR Index for Ranking Scientists’ Publication Impacts: A Case Study on Evolutionary Computation Researchers. pp. 24582465, 2019.
Chaiwanarom, P. and Lursinsap, C. Collaborator recommendation in interdisciplinary computer science using degrees of collaborative forces, temporal evolution of research interest, and comparative seniority status. Knowledge-Based Systems vol. 75, pp. 161172, 2015. Publisher: Elsevier.
Davison, E. and Price, J. How do we rate? An evaluation of online student evaluations. Assessment & Evaluation in Higher Education 34 (1): 5165, 2009. Publisher: Taylor & Francis.
Dorogovtsev, S. N. and Mendes, J. F. Ranking scientists. Nature Physics 11 (11): 882883, 2015. Publisher: Nature Publishing Group.
Gao, B. and Kumar, G. CoRank: Simultaneously Ranking Publication Venues and Researchers. pp. 60556057, 2019.
Ghani, R., Qayyum, F., Afzal, M., and Maurer, H. Comprehensive evaluation of hindex and its extensions in the domain of mathematics. Scientometrics 118 (3): 809822, 2019.
Huang, W., Wu, Z., Liang, C., Mitra, P., and Giles, C. A neural probabilistic model for context based citation recommendation. Vol. 29, 2015. Issue: 1.
Jin, B., Liang, L., Rousseau, R., and Egghe, L. The Rand ARindices: Complementing the hindex. Chinese science bulletin 52 (6): 855863, 2007. Publisher: Springer.
Khanam, Z. and Alkhaldi, S. An Intelligent Recommendation Engine for Selecting the University for Graduate Courses in KSA: SARS Student Admission Recommender System. Lecture Notes in Networks and Systems vol. 98, pp. 711722, 2020.
Liang, D., Charlin, L., McInerney, J., and Blei, D. M. Modeling user exposure in recommendation. pp. 951961, 2016.
Lima, H., Silva, T. H., Moro, M. M., Santos, R. L., Meira Jr, W., and Laender, A. H. Aggregating productivity indices for ranking researchers across multiple areas. pp. 97106, 2013.
Maqsood, S., Islam, M., Afzal, M., and Masood, N. A comprehensive author ranking evaluation of network and bibliographic indices. Malaysian Journal of Library and Information Science 25 (1): 3145, 2020.
Oliveira, E., Gomes Basoni, H., Rodrigues Saúde, M., and Marques Ciarelli, P. Combining Clustering and Classification Approaches for Reducing the Effort of Automatic Tweets Classification:. In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval. SCITEPRESS Science and and Technology Publications, Rome, Italy, pp. 465472, 2014.
Pradhan, T. and Pal, S. CNAVER: A Content and Networkbased Academic VEnue Recommender system. KnowledgeBased Systems vol. 189, 2020.
Rathore, M. M. U., Gul, M. J. J., Paul, A., Khan, A. A., Ahmad, R. W., Rodrigues, J., and Bakiras, S. Multilevel graphbased decision making in big scholarly data: An approach to identify expert reviewer, finding quality impact factor, ranking journals and researchers. IEEE Transactions on Emerging Topics in Computing, 2018.
Rost, K. and Frey, B. S. Quantitative and qualitative rankings of scholars. Schmalenbach Business Review 63 (1): 6391, 2011. Publisher: Springer.
Sabour, S. H. Index, an ugly truth. Shiraz E Medical Journal 20 (5), 2019.
Sebastian, Y., Siew, E., and Orimaye, S. O. Learning the heterogeneous bibliographic information network for literaturebased discovery. KnowledgeBased Systems vol. 115, pp. 6679, 2017. Publisher: Elsevier.
Shah, K., Salunke, A., Dongare, S., and Antala, K. Recommender systems: An overview of different approaches to recommendations. IEEE, pp. 14, 2017.
Sharma, M. and Mann, S. A survey of recommender systems: approaches and limitations. International Journal of Innovations in Engineering and Technology 2 (2): 814, 2013. Publisher: Citeseer.
Son, J. and Kim, S. B. Academic paper recommender system using multilevel simultaneous citation networks. Decision Support Systems vol. 105, pp. 2433, 2018. Publisher: Elsevier.
Ströele, V., Campos, F., David, J. M. N., Braga, R., Abdalla, A., Lancellotta, P. I., Zimbrão, G., and Souza, J. Data abstraction and centrality measures to scientific social network analysis. In 2017 IEEE 21st International Conference on Computer Supported Cooperative Work in Design (CSCWD). IEEE, pp. 281286, 2017.
Tang, J., Wu, S., Sun, J., and Su, H. Crossdomain collaboration recommendation. pp. 12851293, 2012.
Tatiya, R. V. and Vaidya, A. S. A survey of recommendation algorithms. IOSR J. Comput. Eng 16 (6): 1619, 2014.
Wang, G., He, X., and Ishuga, C. I. HARSI: A novel hybrid article recommendation approach integrating with social information in scientific social network. KnowledgeBased Systems vol. 148, pp. 8599, 2018. Publisher: Elsevier.
Yang, Z., Yin, D., and Davison, B. D. Recommendation in academia: A joint multirelational model. IEEE, pp. 566571, 2014.
Yu, S., Liu, J., Yang, Z., Chen, Z., Jiang, H., Tolba, A., and Xia, F. PAVE: Personalized Academic Venue recommendation Exploiting copublication networks. Journal of Network and Computer Applications vol. 104, pp. 3847, 2018.
Zriaa, R. and Amali, S. A Comparative Study Between KNearest Neighbors and KMeans Clustering Techniques of Collaborative Filtering in eLearning Environment. Lecture Notes in Networks and Systems vol. 183, pp. 268282, 2021.
Publicado
04/10/2021
Como Citar
LIMA, Jaimel de Oliveira; OLIVEIRA, Elias.
Combining Clustering and Regression Models for Recommending Researchers. In: SYMPOSIUM ON KNOWLEDGE DISCOVERY, MINING AND LEARNING (KDMILE), 9. , 2021, Rio de Janeiro.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 137-144.
ISSN 2763-8944.
DOI: https://doi.org/10.5753/kdmile.2021.17471.
