Incremental Refinement of Relevance Rankings: Introducing a New Method Supported with Pennant Retrieval

AKBULUT M., Tonta Y.

TURKISH LIBRARIANSHIP, cilt.36, sa.2, ss.169-203, 2022 (ESCI) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 36 Sayı: 2
  • Basım Tarihi: 2022
  • Doi Numarası: 10.24146/tk.1062751
  • Dergi Adı: TURKISH LIBRARIANSHIP
  • Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Library and Information Science Abstracts, Library, Information Science & Technology Abstracts (LISTA), Directory of Open Access Journals, TR DİZİN (ULAKBİM)
  • Sayfa Sayıları: ss.169-203
  • Anahtar Kelimeler: Relevance rankings, probabilistic topic modeling, the Latent Dirichlet Allocation (LDA) algorithm, pennant retrieval, Maximal Marginal Relevance (MMR), INTERDISCIPLINARY RESEARCH, COMBINING BIBLIOMETRICS, INFORMATION-RETRIEVAL, COCITATION, DISCOVERY, EXAMPLES, MODEL, TERM
  • Hacettepe Üniversitesi Adresli: Evet

Özet

Purpose: Relevance ranking algorithms rank retrieved documents based on the degrees of topical similarity (relevance) between search queries and documents. This paper aims to introduce a new relevance ranking method combining a probabilistic topic modeling algorithm with citation data.