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New research evaluates unsupervised methods for scholarly collaboration recommendations

By PulseAugur Editorial · [1 sources] ·

Researchers have evaluated unsupervised methods for recommending scholarly collaborations based on publication text. The study compared TF-IDF, topic-based models (LDA, BERTopic), and embedding-based retrieval using SciBERT with Faiss. Results indicated that topic and embedding-based approaches maintained stable performance even when publication overlap was reduced, suggesting they capture broader similarities beyond direct lexical matches. The paper also explored explainability through intrinsic topic-based and post-hoc retrieval-based methods, offering complementary insights. AI

RANK_REASON The cluster contains an academic paper detailing research findings on scholarly collaboration recommendations. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New research evaluates unsupervised methods for scholarly collaboration recommendations

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The cluster contains an academic paper detailing research findings on scholarly collaboration recommendations. [lever_c_demoted from research: ic=1 ai=1.0]
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109 days old
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Jason A. Clark ·

    Evaluation and Explainability of Unsupervised Scholarly Collaboration Recommendations

    In this paper, we examine unsupervised, content-based collaboration recommendations using publication text in scholarly settings. We compare three families of methods: a TF-IDF baseline, topic-based models (LDA and BERTopic, including clone variants), and embedding-based retrieva…

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