2007/04/23 by Stefan Pohl, Pohl, Stefan · 1 citation
Computer Science · #Computer science #Data Mining Algorithms and Applications #Data science #Digital Libraries (cs.DL) #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Information retrieval #Internet privacy #Recommender Systems and Techniques #Web Data Mining and Analysis #cs.DL #cs.IR
paper · pdf · doi:10.48550/arxiv.0704.2963
73 pages, 31 figures, Master's Thesis
arxiv created 2007/04/23 · openalex publication_date 2007/04/23 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
This thesis investigates in the use of access log data as a source of information for identifying related scientific papers. This is done for arXiv.org, the authority for publication of e-prints in several fields of physics. Compared to citation information, access logs have the advantage of being immediately available, without manual or automatic extraction of the citation graph. Because of that, a main focus is on the question, how far user behavior can serve as a replacement for explicit meta-data, which potentially might be expensive or completely unavailable. Therefore, we compare access, content, and citation-based measures of relatedness on different recommendation tasks. As a final result, an online recommendation system has been built that can help scientists to find further relevant literature, without having to search for them actively.