Formação de Equipes Profissionais através da Avaliação da Similaridade entre Currículos
Resumo
Este artigo apresenta um método para identificar a similaridade entre pessoas a partir da análise de seus currículos. O objetivo do trabalho é através de uma função de similaridade encontrar pessoas que possam realizar uma terminada tarefa ou projeto. O trabalho compara 3 formas de análise: a análise dos títulos das publicações, a análise das palavras-chave e a análise do texto das publicações. A aplicação deste método permite dar suporte aos sistemas de recomendação para oferecerem fontes de informações mais relevantes aos usuários.
Palavras-chave:
Formação de Equipes Profissionais, Avaliação da Similaridade, Currículos
Referências
ADOMAVICIUS, G. and TUZHILIN, A. (2005) “Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions”. IEEE Transactions on Knowledge and Data Engineering, v. 17, n. 6, p. 734-749.
BRUTLAG, J.D. and MEEK, C. (2000) “Challenges of the email domain for text classification”. In: 7th International Conference on Machine Learning (ICML 2000), Stanford University, USA, p. 103-110
CNPq, Conselho Nacional de Pesquisa e qualidade. Disponível pela URL: http://lattes.cnpq.br/
DAVENPORT, T. H. e PRUZAC, L. (1997) “Working knowledge – How organizations Manage what they know”, Harvard Business School Press, Harvard.
DUMAIS, S. T. and NIELSEN, J. “Automating the assignment of submitted manuscripts to reviewers”. In 15th International ACM SIGIR Conference on Research and Development in Information Retrieval, Copenhagen, Denmark, p. 233-244.
GOLDBERG, D.; NICHOLS, D.; OKI, B. M. and TERRY, D. “Using collaborative filtering to weave an information taperstry”. Commun. ACM, New York, NY, USA, v.35, n.12, p61-70, 1992.
HERLOCKER, J.L.; KONSTAN, J.A.; BORCHERS, A. and RIEDL, J. “An algorithmic framework for performing collaborative filtering”. In: SIGIR '99 Proceedings of the 22nd annual international ACM SIGIR conference on research and development in information retrieval, 1999, New York, NY, USA. Anais... ACM Press, 1999. p.230-237.
HILL, W.; STEAD, L.; ROSENSTEIN, M. and FURNAS, G. “Recommending and evaluating choices in a virtual community of use”. In: CHI’95: Proceedings of the sigchi conference on human factors in computing systems, 1995, New York, NY, USA. Anais... ACM Press/Addison-Wesley Publishing Co.,1995. p.194–201.
KOWALSKI, G. “Information Retrieval Systems: Theory and Implementation”. Norwell, MA, USA: Kluwer Academic Publishers, 1997.
RASHID, A. M.; MCNEE, S.M.; ALBERT, I.; COSLEY, D.; GOPALKRISHNAN, P.;
LAM, S. K.; KONSTAN, J. A. and RIEDL, J. (2002) “On the recommending of citations for research papers”.In Proceedings of the 2002 ACM Conference on Computer Supported Cooperative Work, p.116-125.
RESNICK, P.; IACOVOU, N.; SUCHAK, M.; BERGSTORM, P. and RIEDL, J.GroupLens: “An Open Architecture for Collaborative Filtering of Netnews”. In: Proceedings of ACM 1994 Conference on Computer Supported Cooperative Work, 1994, Chapel Hill, North Carolina. Anais... ACM,1994. p.175–186.
SAMPAIO, I.; RAMALHO, G.; CORRUBLE, V. and PRUDÊNCIO, R. (2006) “Acquiring the preferences of new users in recommender systems: the role of item controversy”. In Proceedings of the Workshop on Recommender Systems (in conjunction with the 17th European Conference on Artificial Intelligence - ECAI 2006). Riva del Garda, Italy, August 2006, p.107-110.
SHARDANAND, U. and MAES, P. “Social Information Filtering: Algorithms for Automating WordofMouth”. In: ACM CHI’95 Conference on human factors in computing systems, 1995. Proceedings...[S.l.:s.n.], 1995.v.1,p.210–217.
SPERTUS, E.; SAHAMI, M. and BUYUKKOKTEN, O. “Evaluating similarity measures: a large-scale study in the orkut social network”. Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery and data mining KDD '05, August 2005, p.678-684.
STOILOVA, L.; HOLLOWAY, T.; MARKINES, B.; MAGUITMAN, A. G. and MENCZER, F. (2005) “GiveALink: mining a semantic network of bookmarks for web search and recommendation”. In Proceedings of the 3rd International Workshop on Link discovery LinkKDD, August 2005, p. 66-73.
TORRES, R. “Combining Collaborative and Content-based Filtering to Recommend Research Papers” [S.1.].
VANRIJSBERGEN, C. J. “Information Retrieval, 2nd edition”. [S.l.]: Dept. of Computer Science, University of Glasgow, 1979.
BRUTLAG, J.D. and MEEK, C. (2000) “Challenges of the email domain for text classification”. In: 7th International Conference on Machine Learning (ICML 2000), Stanford University, USA, p. 103-110
CNPq, Conselho Nacional de Pesquisa e qualidade. Disponível pela URL: http://lattes.cnpq.br/
DAVENPORT, T. H. e PRUZAC, L. (1997) “Working knowledge – How organizations Manage what they know”, Harvard Business School Press, Harvard.
DUMAIS, S. T. and NIELSEN, J. “Automating the assignment of submitted manuscripts to reviewers”. In 15th International ACM SIGIR Conference on Research and Development in Information Retrieval, Copenhagen, Denmark, p. 233-244.
GOLDBERG, D.; NICHOLS, D.; OKI, B. M. and TERRY, D. “Using collaborative filtering to weave an information taperstry”. Commun. ACM, New York, NY, USA, v.35, n.12, p61-70, 1992.
HERLOCKER, J.L.; KONSTAN, J.A.; BORCHERS, A. and RIEDL, J. “An algorithmic framework for performing collaborative filtering”. In: SIGIR '99 Proceedings of the 22nd annual international ACM SIGIR conference on research and development in information retrieval, 1999, New York, NY, USA. Anais... ACM Press, 1999. p.230-237.
HILL, W.; STEAD, L.; ROSENSTEIN, M. and FURNAS, G. “Recommending and evaluating choices in a virtual community of use”. In: CHI’95: Proceedings of the sigchi conference on human factors in computing systems, 1995, New York, NY, USA. Anais... ACM Press/Addison-Wesley Publishing Co.,1995. p.194–201.
KOWALSKI, G. “Information Retrieval Systems: Theory and Implementation”. Norwell, MA, USA: Kluwer Academic Publishers, 1997.
RASHID, A. M.; MCNEE, S.M.; ALBERT, I.; COSLEY, D.; GOPALKRISHNAN, P.;
LAM, S. K.; KONSTAN, J. A. and RIEDL, J. (2002) “On the recommending of citations for research papers”.In Proceedings of the 2002 ACM Conference on Computer Supported Cooperative Work, p.116-125.
RESNICK, P.; IACOVOU, N.; SUCHAK, M.; BERGSTORM, P. and RIEDL, J.GroupLens: “An Open Architecture for Collaborative Filtering of Netnews”. In: Proceedings of ACM 1994 Conference on Computer Supported Cooperative Work, 1994, Chapel Hill, North Carolina. Anais... ACM,1994. p.175–186.
SAMPAIO, I.; RAMALHO, G.; CORRUBLE, V. and PRUDÊNCIO, R. (2006) “Acquiring the preferences of new users in recommender systems: the role of item controversy”. In Proceedings of the Workshop on Recommender Systems (in conjunction with the 17th European Conference on Artificial Intelligence - ECAI 2006). Riva del Garda, Italy, August 2006, p.107-110.
SHARDANAND, U. and MAES, P. “Social Information Filtering: Algorithms for Automating WordofMouth”. In: ACM CHI’95 Conference on human factors in computing systems, 1995. Proceedings...[S.l.:s.n.], 1995.v.1,p.210–217.
SPERTUS, E.; SAHAMI, M. and BUYUKKOKTEN, O. “Evaluating similarity measures: a large-scale study in the orkut social network”. Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery and data mining KDD '05, August 2005, p.678-684.
STOILOVA, L.; HOLLOWAY, T.; MARKINES, B.; MAGUITMAN, A. G. and MENCZER, F. (2005) “GiveALink: mining a semantic network of bookmarks for web search and recommendation”. In Proceedings of the 3rd International Workshop on Link discovery LinkKDD, August 2005, p. 66-73.
TORRES, R. “Combining Collaborative and Content-based Filtering to Recommend Research Papers” [S.1.].
VANRIJSBERGEN, C. J. “Information Retrieval, 2nd edition”. [S.l.]: Dept. of Computer Science, University of Glasgow, 1979.
Publicado
08/11/2006
Como Citar
GRANADA, Roger Leitzke; REGO, Paulo Roberto Faulstich; LOH, Stanley; LICHTNOW, Daniel; BORGES, Thyago; GUIMARÃES, Gabriel.
Formação de Equipes Profissionais através da Avaliação da Similaridade entre Currículos. In: SIMPÓSIO BRASILEIRO DE SISTEMAS DE INFORMAÇÃO (SBSI), 3. , 2006, Curitiba.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2006
.
p. 97-104.
DOI: https://doi.org/10.5753/sbsi.2006.14732.
