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This thesis investigates in the use of access log data as a source ofinformation for identifying related scientific papers. This is done forarXiv.org, the authority for publication of e-prints in several fields ofphysics.  Compared to citation information, access logs have the advantage of beingimmediately available, without manual or automatic extraction of the citationgraph. Because of that, a main focus is on the question, how far user behaviorcan serve as a replacement for explicit meta-data, which potentially might beexpensive or completely unavailable. Therefore, we compare access, content, andcitation-based measures of relatedness on different recommendation tasks. As afinal result, an online recommendation system has been built that can helpscientists to find further relevant literature, without having to search forthem actively.

Using Access Data for Paper Recommendations on ArXiv.org