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This paper presents an approach for identifying similar documents that can be used to assist scientists in finding related work. The approach called Citation Proximity Analysis (CPA) is a further development of co-citation analysis, but in addition, considers the proximity of citations to each other within an article " s full-text. The underlying idea is(More)
This article introduces and discusses the concept of academic search engine optimization (ASEO). Based on three recently conducted studies, guidelines are provided on how to optimize scholarly literature for academic search engines in general and for Google Scholar in particular. In addition, we briefly discuss the risk of researchers' illegitimately(More)
Plagiarism Detection Systems have been developed to locate instances of plagiarism e.g. within scientific papers. Studies have shown that the existing approaches deliver reasonable results in identifying copy&paste plagiarism, but fail to detect more sophisticated forms such as paraphrased plagiarism, translation plagiarism or idea plagiarism. The(More)
Google Scholar is one of the major academic search engines but its ranking algorithm for academic articles is unknown. In a recent study we partly reverse-engineered the algorithm. This paper presents the results of our second study. While the previous study provided a broad overview, the current study focused on analyzing the correlation of an article's(More)
— Google Scholar is one of the major academic search engines but its ranking algorithm for academic articles is unknown. We performed the first steps to reverse-engineering Google Scholar's ranking algorithm and present the results in this research-in-progress paper. The results are: Citation counts is the highest weighed factor in Google Scholar's ranking(More)
In a previous paper we provided guidelines for scholars on optimizing research articles for academic search engines such as Google Scholar. Feedback in the academic community to these guidelines was diverse. Some were concerned researchers could use our guidelines to manipulate rankings of scientific articles and promote what we call 'academic search engine(More)
Over 80 approaches for academic literature recommendation exist today. The approaches were introduced and evaluated in more than 170 research articles, as well as patents, presentations and blogs. We reviewed these approaches and found most evaluations to contain major shortcomings. Of the approaches proposed, 21% were not evaluated. Among the evaluated(More)
This paper evaluates the performance of tools for the extraction of metadata from scientific articles. Accurate metadata extraction is an important task for automating the management of digital libraries. This comparative study is a guide for developers looking to integrate the most suitable and effective metadata extraction tool into their software. We(More)
Offline evaluations are the most common evaluation method for research paper recommender systems. However, no thorough discussion on the appropriateness of offline evaluations has taken place, despite some voiced criticism. We conducted a study in which we evaluated various recommendation approaches with both offline and online evaluations. We found that(More)
—This work-in-progress paper introduces two new approaches called Citation Proximity Analysis (CPA) and Citation Order Analysis (COA). They can be applied to identify related documents for the purpose of research paper recommender systems. CPA is a variant of co-citation analysis that additionally considers the proximity of citations to each other within an(More)