Inter-departmental research collaboration recommender system based on content filtering in a cold start problem

Purwitasari, Diana; Fatichah, Chastine; Purnama, I Ketut Eddy; Sumpeno, Surya; Purnomo, Mauridhi Hery

doi:10.1109/iwcia.2017.8203581

Cited by 8 publications

(10 citation statements)

References 15 publications

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“…ere have not been studies for forming interinstitutional research teams. Although Purwitasari et al [17] have proposed a team formation method for interdepartmental research collaboration, this method considers only topics of publications and does not consider departments/institutes as one of the stakeholders of research. Hernandez-Gress et al [18] analyzed bibliographic data to recommend collaborations between universities by using only research topics of each scholar.…”

Section: Related Workmentioning

confidence: 99%

Interinstitutional Research Team Formation Based on Bibliographic Network Embedding

Lee

Hong

Kim

2021

Mobile Information Systems

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This study aims at forming research teams for interinstitutional collaborations. Research institutes have their own purposes and topics of interest. Thus, supporting joint research between multiple institutes, we have to consider not only synergies between scholars but also purposes of the institutes. To solve this problem, we propose a bibliographic network embedding method that can learn characteristics of institutes, not only of each scholar. First, we compose a bibliographic network that consists of scholars, publications, venues, research projects, and institutes. Collaboration styles and research topics of institutes and scholars are extracted by mining subgraphs from the bibliographic network. Then, vector representations of network nodes are learned based on occurrences of subgraphs on the nodes and neighborhoods of the nodes. Based on the vector representations, we train multilayer perceptrons (MLP) to assess collaboration probability between scholars affiliated in different institutes. For training the MLP, we suggest three strategies: (i) considering every collaboration, (ii) focusing on interinstitutional collaborations, and (iii) focusing on collaboration outcomes. To evaluate the proposed methods, we have analyzed research collaborations of POSTECH (Pohang University of Science and Technology) and RIST (Research Institute of Industrial Science and Technology) from 2011 to 2020. Then, we conducted the research team formation for joint research of the two institutes according to two purposes: pure research and commercialization research.

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Section: Related Workmentioning

confidence: 99%

Interinstitutional Research Team Formation Based on Bibliographic Network Embedding

Lee

Hong

Kim

2021

Mobile Information Systems

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“…Peneliti yang memiliki kinerja publikasi penelitian yang bagus memberikan dampak yang signifikan dalam perkembangan suatu bidang penelitian karena peneliti tersebut memiliki banyak pengalaman dalam mengeksplorasi bidang yang menjadi fokus peneliti. Dalam aktivitas penelitian, peneliti tersebut dapat dijadikan sebagai teman kolaborasi yang sesuai dengan fokus bidang peneliti [1] atau bahkan dengan fokus bidang yang agak berbeda [2]. Ada beberapa metode yang dapat digunakan untuk mengukur kinerja publikasi penelitian seorang peneliti.…”

Section: Pendahuluanunclassified

“…Untuk menghitung 11 dari 1 dan 4 , digunakan persamaan (1). Untuk menghitung 11 dari 2 dan 5 , digunakan persamaan (2). Untuk menghitung 11 dari 3 dan 6 , digunakan persamaan (3).…”

Section: Ekstraksi Fiturunclassified

Ekstraksi Fitur Produktivitas Dinamis berdasarkan Topik Artikel Ilmiah untuk Klasterisasi Peneliti

Haniefardy

Purwitasari

Fatichah

2021

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Pengelompokkan peneliti seringkali menggunakan informasi tekstual yang terdapat pada artikel ilmiah peneliti, contohnya judul, abstrak, dan kata kunci sehingga menghasilkan kelompok peneliti dengan kemiripan informasi tekstual pada artikel ilmiah mereka. Pengelompokkan peneliti juga seringkali menggunakan jumlah publikasi dan sitasi sehingga menghasilkan kelompok peneliti yang memiliki jumlah publikasi dan sitasi yang cenderung sama. Berdasarkaan kedua metode di atas, penelitian ini mencoba untuk menganalisis penggunaan topik artikel ilmiah pada proses ekstraksi fitur produktivitas. Fitur ini merupakan fitur yang didapatkan melalui penghitungan kinerja peneliti berdasarkan jumlah publikasi dan sitasi. Hasil ekstraksi fitur akan digunakan untuk klasterisasi peneliti menggunakan metode K-Means++. Sebelum data peneliti diklasterisasi, terlebih dahulu data peneliti dianalisis untuk menghilangkan kemungkinan adanya outlier. Evaluasi hasil klaster dilakukan dengan mempertimbangkan nilai Sum Squared Error dan Silhouette. Hasilnya, klaster optimal didapatkan dengan nilai K sama dengan 8 dan nilai silhouette sama dengan 0.15396. Kemudian, hasil klaster dianalisis untuk dapat memberikan label terhadap masing-masing klaster dengan mempertimbangkan topik artikel ilmiah, jumlah publikasi dan jumlah sitasi.

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“…Pakar dapat memiliki banyak fokus topik penelitian, antara lain sebagai hasil kolaborasi antar domain [9]. Penelitian yang telah ada membuat pemodelan pakar dengan graf, tetapi tidak mengakomodasi topik penelitian [2].…”

Section: Pendahuluanunclassified

“…Untuk ciri , fungsi ( ) adalah jumlah sitasi yang diperoleh di tahun pengamatan dengan catatan dokumen tersebut masuk dalam klaster topik . Aspek dinamis di setiap ciri produktivitas (misalnya ) pada suatu topik tertentu untuk menangkap kinerja pakar adalah perubahan mínimum (min), maksimum (max), terakhir (end), serta total perubahan (sum) di (6)- (9). Kemudian nilai representatif kinerja keseluruhan pakar (rep) untuk F1 dan F4 dihitung dengan (10), untuk F2 dan F5 dengan (11), serta untuk F3 dan F6 dengan (12).…”

Section: B Ekstraksi Fitur Produktivitasunclassified

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

Purwitasari¹,

Fatichah²,

Sumpeno³

et al. 2018

Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI)

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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.

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Inter-departmental research collaboration recommender system based on content filtering in a cold start problem

Cited by 8 publications

References 15 publications

Interinstitutional Research Team Formation Based on Bibliographic Network Embedding

Interinstitutional Research Team Formation Based on Bibliographic Network Embedding

Ekstraksi Fitur Produktivitas Dinamis berdasarkan Topik Artikel Ilmiah untuk Klasterisasi Peneliti

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

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