Combining Clustering and Regression Models for Recommending Researchers


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


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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: