Akbulut M., Tonta Y.
JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY, cilt.0, sa.0, ss.1-24, 2026 (SCI-Expanded, SSCI, Scopus)
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Yayın Türü:
Makale / Tam Makale
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Cilt numarası:
0
Sayı:
0
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Basım Tarihi:
2026
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Doi Numarası:
10.1002/asi.70101
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Dergi Adı:
JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY
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Derginin Tarandığı İndeksler:
Applied Science & Technology Source, Library Literature and Information Science, Information Science & Technology Abstracts (LISTA), Academic Search Ultimate (EBSCO), Social Science Premium Collection (ProQuest), Business Source Ultimate (EBSCO), Education Collection (ProQuest), Education Source Ultimate (EBSCO), Engineering Source (EBSCO), Scopus, Library & Information Science Collection (ProQuest), Technology Collection (ProQuest), Aerospace Database, Agricultural & Environmental Science Database, Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Periodicals Index Online, ABI/INFORM, CINAHL, Compendex, Education Abstracts, Information Science and Technology Abstracts, INSPEC, EBSCO Education Source, Library, Information Science & Technology Abstracts (LISTA), MLA - Modern Language Association Database, MLA International Bibliography
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Sayfa Sayıları:
ss.1-24
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Ankara Yıldırım Beyazıt Üniversitesi Adresli:
Evet
Özet
Abstract
Delivering both relevant and topically diverse results is a key challenge in information retrieval (IR). This study introduces a hybrid method that incrementally refines rankings by combining probabilistic topic modeling (latent dirichlet allocation [LDA]) with citation‐based pennant retrieval grounded in Relevance Theory (RT), optimizing for both relevance and diversity. A unified scoring function fuses topic and citation cues, while a tunable weight parameter makes the relevance–diversity trade‐off explicit and transparent. Applying this RT‐guided approach to the iSearch corpus (435,000 physics papers, 3.7 million citations) across 65 queries, we evaluate relevance and diversity via degree centrality, arXiv subject categories, and maximal marginal relevance (MMR). Unlike prior methods that apply LDA or pennant retrieval separately—or depend on post‐hoc diversification such as MMR—this approach integrates them directly and efficiently: LDA runs on local multi‐core CPUs, and pennant retrieval operates using sparse co‐citation counts. The combined technique retrieves both central and peripheral works, producing a boundary‐spanning effect that broadens conceptual coverage while improving normalized discounted cumulative gain (NDCG) performance. Interactive visualizations via LuminaCite depict how researchers can fine‐tune results in real time to reveal latent or interdisciplinary connections. Sensitivity and robustness tests indicate that the proposed model is robust to hyperparameter variation, first‐stage retriever choice, and query reformulation.