Ekstraksi Ciri Produktivitas Dinamis untuk Prediksi Topik Pakar dengan Model Discrete Choice

Diana Purwitasari, Chastine Fatichah, Surya Sumpeno, Mauridhi Hery Purnomo


Recommendation of active or productive experts is indispensable in supporting collaborations. Activities of publication and citation indicate expert productivity. An expert can be inferred to have an interest in a subject through productivity in that particular topic. Since an expert can change interests over time, the contribution of this paper is a Discrete Choice Model (DCM) based on topic productivities to predict the primary interests of the experts. DCM uses features extracted from bibliographic data of citation relation and title-abstract texts. Before extracting productivity features and dynamicity features to represent interest changes, title clustering with KMeans++ is used to identify research topics. There are six productivity features and five dynamicity values for each productivity feature to demonstrate the expert behavior. Therefore, a clustered topic as a research interest is represented as an expert choice with 30 extracted features in the proposed method. The experiments used multinomial logistic regression for DCM and a log-likelihood indicator for the fitted models of the features. The resulted DCM models showed that productive behavior of the experts by doing many publications and receiving many citations effected to the precision of topic prediction by 80%. Some features were better for predicting primary interests of the expert. It was demonstrated with a lower precision value of 60% by using features that represent the expert behavior of only doing publication or only getting citation.


prediksi topik, ekstraksi ciri produktivitas, profil pakar, model discrete choice, data bibliografi.

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K. Balog, Y. Fang, M. de Rijke, P. Serdyukov, dan L. Si, “Expertise Retrieval,” Found. Trends Inf. Retr., Vol. 6, No. 2–3, hal. 127–256, Feb. 2012.

F. Xia, Z. Chen, W. Wang, J. Li, dan L.T. Yang, “MVCWalker: Random Walk-Based Most Valuable Collaborators Recommendation Exploiting Academic Factors,” IEEE Trans. Emerg. Top. Comput., Vol. 2, No. 3, hal. 364–375, Sep. 2014.

F. Alarfaj, U. Kruschwitz, D. Hunter, and C. Fox, “Finding the Right Supervisor: Expert-Finding in a University Domain,” Proc. 2012 Conf. North Am. Chapter Assoc. Comput. Linguist. Hum. Lang. Technol. Student Res. Work, 2012., hal. 1–6.

F. Xia, W. Wang, T.M. Bekele, dan H. Liu, “Big Scholarly Data: A Survey,” IEEE Trans. Big Data, Vol. 3, No. 1, hal. 18–35, Mar. 2017.

S. Lin, W. Hong, D. Wang, dan T. Li, “A Survey on Expert Finding Techniques,” J. Intell. Inf. Syst., Vol. 49, No. 2, hal. 255–279, Oct. 2017.

H. Deng, I. King, dan M.R. Lyu, “Formal Models for Expert Finding on DBLP Bibliography Data,” 2008 Eighth IEEE International Conference on Data Mining, 2008, hal. 163–172.

Z. Yang, J. Tang, B. Wang, J. Guo, dan J. Li, “Expert2Bólè : From Expert Finding to Bólè Search,” Proc. 15th ACM Conf. Knowl. Discov. data Min., 2009, hal. 1–4.

J. Tang, “AMiner: Toward Understanding Big Scholar Data,” Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, 2016, hal. 467.

D. Purwitasari, C. Fatichah, I. K. E. Purnama, S. Sumpeno, dan M.H. Purnomo, “Inter-Departmental Research Collaboration Recommender System Based on Content Filtering in a Cold Start Problem,” 2017 IEEE 10th International Workshop on Computational Intelligence and Applications, IWCIA 2017 - Proceedings, 2017, hal. 177-184.

L. Guo, X. Cai, F. Hao, D. Mu, C. Fang, dan L. Yang, “Exploiting Fine-Grained Co-Authorship for Personalized Citation Recommendation,” IEEE Access, Vol. 5, hal. 12714–12725, 2017.

G. Panagopoulos, G. Tsatsaronis, dan I. Varlamis, “Detecting Rising Stars in Dynamic Collaborative Networks,” J. Informetr., Vol. 11, No. 1, hal. 198–222, 2017.

H. Jiang, “A Nested Logit-Based Approach to Measuring Air Shopping Screen Quality and Predicting Market Share,” J. Revenue Pricing Manag., Vol. 8, No. 2, hal. 134–147, Mar. 2009.

H. Jiang, X. Qi, dan H. Sun, “Choice-Based Recommender Systems: A Unified Approach to Achieving Relevancy and Diversity,” Oper. Res., Vol. 62, No. 5, hal. 973–993, Oct. 2014.

M. Paredes, E. Hemberg, U. O’Reilly, dan C. Zegras, “Machine Learning or Discrete Choice Models for Car Ownership Demand Estimation and Prediction?,” 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), 2017, hal. 780–785.

A. Nurilham, D. Purwitasari, dan C. Fatichah, “Ekstraksi Frasa pada Pelabelan Kelompok Artikel Ilmiah dengan Penggabungan Klaster berdasarkan MaximumCommonSubgraph,” J. Nas. Tek. Elektro dan Teknol. Inf., Vol. 7, No. 3, hal. 249-257, 2018.

M. Ben-Akiva dan S.R. Lerman, Discrete Choice Analysis : Theory and Application to Travel Demand, Cambridge, USA: MIT Press, 1985.

V. Aguirregabiria dan P. Mira, “Dynamic Discrete Choice Structural Models: A Survey,” J. Econom., Vol. 156, No. 1, hal. 38–67, 2010.

E. Lancsar, J. Louviere, C. Donaldson, G. Currie, dan L. Burgess, “Best Worst Discrete Choice Experiments in Health: Methods and an Application,” Soc. Sci. Med., Vol. 76, hal. 74–82, 2013.

G. Antonini, M. Bierlaire, dan M. Weber, “Discrete Choice Models of Pedestrian Walking Behavior,” Transp. Res. Part B Methodol., Vol. 40, No. 8, hal. 667–687, 2006.

D. McFadden, “The Measurement of Urban Travel Demand,” J. Public Econ., Vol. 3, No. 4, hal. 303–328, 1974.

D.R. Radev, P. Muthukrishnan, V. Qazvinian, dan A. Abu-Jbara, “The ACL Anthology Network Corpus,” Lang. Resour. Eval., Vol. 47, No. 4, hal. 919–944, Dec. 2013.

J. Santoso et al., “Self-Training Naive Bayes Berbasis Word2Vec untuk Kategorisasi Berita Bahasa Indonesia,” J. Nas. Tek. Elektro dan Teknol. Inf., vol. 7, no. 2, pp. 158–166, 2018.

A. Zaini, M. A. Muslim, dan Wijono, “Pengelompokan Artikel Berbahasa Indonesia Berdasarkan Struktur Laten Menggunakan Pendekatan Self Organizing Map,” J. Nas. Tek. Elektro dan Teknol. Inf., Vol. 6, No. 3, hal. 259–267, 2017.

O. Somantri dan M. Khambali, “Feature Selection Klasifikasi Kategori Cerita Pendek Menggunakan Naïve Bayes dan Algoritme Genetika,” J. Nas. Tek. Elektro dan Teknol. Inf., Vol. 6, No. 3, hal. 301–306, 2017.

R. Řehůřek dan P. Sojka, “Software Framework for Topic Modelling with Large Corpora,” Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, 2010, hal. 45–50.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, dan D. Cournapeau, “Scikit-learn: Machine Learning in Python,” J. Mach. Learn. Res., Vol. 12, hal. 2825–2830, 2011.

P. J. Rousseeuw, “Silhouettes: A graphical aid to the interpretation and validation of cluster analysis,” J. Comput. Appl. Math., Vol. 20, hal. 53–65, 1987.

DOI: http://dx.doi.org/10.22146/jnteti.v7i4.460


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