Co-author Recommender System

Makarov, Ilya; Bulanov, Oleg; Zhukov, Leonid

doi:10.1007/978-3-319-56829-4_18

Cited by 24 publications

(8 citation statements)

References 15 publications

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“…We have improved recommender systems (Makarov, Bulanov & Zhukov, 2017;Makarov et al, 2018a) based on choosing proper link embedding operator (Makarov et al, 2018b) and including research interest information presented as embedding of nodes in keywords co-occurrence network connecting keywords relating to a given research article. We have compared several machine learning models for future and missing LP problems interpreted as a binary classification problem.…”

Section: Resultsmentioning

confidence: 99%

“…We study the problem of finding collaborator depending on his/her research community, the quality of publications and structural patterns based on co-authorship network suggested by Newman (2004aNewman ( , 2004b. Early unsupervised learning approaches Makarov, Bulanov & Zhukov (2017) and Makarov et al (2018a). In what follows, we describe solution to the LP problem leading to evaluation of our recommender system based on co-authorship network embeddings and manually engineered features for HSE researchers.…”

Section: Introductionmentioning

confidence: 99%

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Dual network embedding for representing research interests in the link prediction problem on co-authorship networks

Makarov

Gerasimova

Sulimov

et al. 2019

PeerJ Computer Science

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We present a study on co-authorship network representation based on network embedding together with additional information on topic modeling of research papers and new edge embedding operator. We use the link prediction (LP) model for constructing a recommender system for searching collaborators with similar research interests. Extracting topics for each paper, we construct keywords co-occurrence network and use its embedding for further generalizing author attributes. Standard graph feature engineering and network embedding methods were combined for constructing co-author recommender system formulated as LP problem and prediction of future graph structure. We evaluate our survey on the dataset containing temporal information on National Research University Higher School of Economics over 25 years of research articles indexed in Russian Science Citation Index and Scopus. Our model of network representation shows better performance for stated binary classification tasks on several co-authorship networks.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dual network embedding for representing research interests in the link prediction problem on co-authorship networks

Makarov

Gerasimova

Sulimov

et al. 2019

PeerJ Computer Science

Self Cite

View full text Add to dashboard Cite

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“…We have improved recommender systems [35,34], based on choosing proper link embedding operator [38] and including research interest information presented as embedding of nodes in keywords co-occurrence network connecting keywords relating to a given research article. We have compared several machine learning models for future and missing link prediction problems interpreted as binary classification problem.…”

Section: Resultsmentioning

confidence: 99%

“…We compare our approach with state-of-the-art algorithms for link prediction problem using structural, attribute and combined feature space to evaluate the impact of the suggested approach on the binary classification task of predicting links in co-authorship network. Such obtained system could be applied for expert search, recommending collaborator or scientific adviser, and searching for relevant research publications similar to the work proposed in [35,34]. In what follows, we describe solution to link prediction problem leading to evaluation of our recommender system based on co-authorship network embeddings and manually engineered features for HSE researchers.…”

Section: Introductionmentioning

confidence: 99%

Peer Review #1 of "Dual network embedding for representing research interests in the link prediction problem on co-authorship networks (v0.2)"

2019

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We present a study on co-authorship network representation based on network embedding together with additional information on topic modeling of research papers and new edge embedding operator. We use link prediction model for constructing recommender system for searching collaborators with similar research interests. Extracting topics for each paper we construct keywords co-occurrence network and use its embedding for further generalizing author attributes. Standard graph feature engineering and network embedding methods were combined for constructing co-author recommender system formulated as link prediction problem and prediction of future graph structure. We evaluate our survey on the dataset containing temporal information on National Research University Higher School of Economics over twenty five years of research articles indexed in Russian Science Citation Index and Scopus. Our model of network representation shows better performance for stated binary classification tasks on several co-authorship networks.

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“…Further methods for identifying existing collaborations between various researchers from various publication databases are presented below [17] developed a co-authorship network to reveal the interactions between researchers. A system for selecting a collaborator with similar research interests for joint research was modeled as a link prediction problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Semantics-based clustering approach for similar research area detection

et al. 2020

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The manual process of searching out individuals in an already existing research field is cumbersome and time-consuming. Prominent and rookie researchers alike are predisposed to seek existing research publications in a research field of interest before coming up with a thesis. From extant literature, automated similar research area detection systems have been developed to solve this problem. However, most of them use keyword-matching techniques, which do not sufficiently capture the implicit semantics of keywords thereby leaving out some research articles. In this study, we propose the use of ontology-based pre-processing, Latent Semantic Indexing and K-Means Clustering to develop a prototype similar research area detection system, that can be used to determine similar research domain publications. Our proposed system solves the challenge of high dimensionality and data sparsity faced by the traditional document clustering technique. Our system is evaluated with randomly selected publications from faculties in Nigerian universities and results show that the integration of ontologies in preprocessing provides more accurate clustering results.

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Co-author Recommender System

Cited by 24 publications

References 15 publications

Dual network embedding for representing research interests in the link prediction problem on co-authorship networks

Dual network embedding for representing research interests in the link prediction problem on co-authorship networks

Peer Review #1 of "Dual network embedding for representing research interests in the link prediction problem on co-authorship networks (v0.2)"

Semantics-based clustering approach for similar research area detection

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