İlgi Sıralamalarının Artırımlı Olarak Geliştirilmesi: Pennant Erişimle Desteklenen Yeni Bir Yöntem Önerisi.

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. Data and Method: We applied this method to the iSearch corpus of c. 435,000 physics papers. We first ran the topic modeling algorithm on titles and summaries of all papers for 65 search queries and obtained the relevance ranking lists. We then used citation data with the existing relevance rankings, thereby incrementally refining the results. The outcome produced better relevance rankings with papers covering various aspects of the topic searched as well as the more marginal ones. Finally, we evaluated the retrieval performance of the proposed method. Findings: Findings suggest that the topic modeling algorithm might sometimes overlook the terms used in different contexts in the papers. However, the fusion of citation data to relevance ranking lists provides additional contextual information, thereby enriching the results further with various papers of higher relevance. Moreover, results can easily be re-ranked. Implications: We argue that once it is tested on dynamic corpora for computational load, robustness, replicability, and scalability, the proposed method can, in time, be used in both local and international information systems such as TR-Dizin, Web of Science, and Scopus. Originality: The proposed method is, as far as we know, the first one that shows that relevance rankings produced with a topic modeling algorithm can be incrementally refined using citation data. [ABSTRACT FROM AUTHOR]