Recommending Scientific Collaboration from ResearchGate

Rodrigues, Marcos W.; Brandão, Wladmir Cardoso; Zárate, Luis Enrique

doi:10.1109/bracis.2018.00065

Cited by 14 publications

(8 citation statements)

References 21 publications

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“…In addition to the research based on the above academic social platforms, research on the ResearchGate social platform has also been conducted. Rodrigues et al [4] adopted the textual similarity of published papers to represent the similarity between scholars. TF-IDF pattern was employed to assess textual similarity.…”

Section: Academic Social Platform Recommendationmentioning

confidence: 99%

“…-TF-IDF: Term Frequency-Inverse Document Frequency [4]. It evaluates the importance of words based on the frequency of feature items and document frequency.…”

Section: Comparative Approachesmentioning

confidence: 99%

“…It evaluates the importance of words based on the frequency of feature items and document frequency. -BOW: Bag of Words [4]. It puts all the words of a corpus into a set.…”

Section: Comparative Approachesmentioning

confidence: 99%

“…-CB: Content-Based [4,22]. This model makes predictions and recommendations based on users' past content preferences.…”

Section: Comparative Approachesmentioning

confidence: 99%

“…Scholar collaboration can massively benefit academic innovation and reduce academic dullness [4]. Scholars also tend to inspire new sparks and ideas in exchanges.…”

Section: Introductionmentioning

confidence: 99%

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Personalized Scholar Recommendation Based on Multi-Dimensional Features

et al. 2021

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The rapid development of social networking platforms in recent years has made it possible for scholars to find partners who share similar research interests. Nevertheless, this task has become increasingly challenging with the dramatic increase in the number of scholar users over social networks. Scholar recommendation has recently become a hot topic. Thus, we propose a personalized scholar recommendation approach, Mul-RSR (Multi-dimensional features based Research Scholar Recommendation), which improves accuracy and interpretability. In this work, Mul-RSR aims to provide personalized recommendation for academic social platforms. Mul-RSR uses the Doc2Vec text model and the random walk algorithm to calculate textual similarity and social relevance to measure the correlation between scholars. It is able to recommend Top-N scholars for each scholar based on multi-layer perception and attention mechanism. To evaluate the proposed approach, we conduct a series of experiments based on public and self-collected ResearchGate datasets. The results demonstrate that our approach improves the recommendation hit rate, and the hit rate reaches 59.31% when the N value is 30. Through these evaluations, we show Mul-RSR can provide a more solid scientific decision-making basis and achieve a better recommendation effect.

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Section: Academic Social Platform Recommendationmentioning

confidence: 99%

“…-TF-IDF: Term Frequency-Inverse Document Frequency [4]. It evaluates the importance of words based on the frequency of feature items and document frequency.…”

Section: Comparative Approachesmentioning

confidence: 99%

“…It evaluates the importance of words based on the frequency of feature items and document frequency. -BOW: Bag of Words [4]. It puts all the words of a corpus into a set.…”

Section: Comparative Approachesmentioning

confidence: 99%

“…-CB: Content-Based [4,22]. This model makes predictions and recommendations based on users' past content preferences.…”

Section: Comparative Approachesmentioning

confidence: 99%

“…Scholar collaboration can massively benefit academic innovation and reduce academic dullness [4]. Scholars also tend to inspire new sparks and ideas in exchanges.…”

Section: Introductionmentioning

confidence: 99%

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