![]() ![]() For instance, the optimal size of an algorithms' user model depended on users' age. Some of the determinants have interdependencies. Determinants we examined include user characteristics (gender and age), datasets, weighting schemes, the time at which recommendations were shown, and user-model size. We found several determinants that may contribute to the large discrepancies observed in recommendation effectiveness. ![]() For example, in one news-recommendation scenario, the performance of a content-based filtering approach was twice as high as the second-best approach, while in another scenario the same content-based filtering approach was the worst performing approach. The experiments show that there are large discrepancies in the effectiveness of identical recommendation approaches in only slightly different scenarios, as well as large discrepancies for slightly different approaches in identical scenarios. We conduct experiments using Plista's news recommender system, and Docear's research-paper recommender system. In this article, we examine the challenge of reproducibility in recommender-system research. However, comparing their effectiveness is a challenging task because evaluation results are rarely reproducible. Numerous recommendation approaches are in use today. At the end of this paper, we discuss whether academic search engine spam could become a serious threat to Web-based academic search engines. The results show that academic search engine spam is indeed-and with little effort-possible: We increased rankings of academic articles on Google Scholar by manipulating their citation counts Google Scholar indexed invisible text we added to some articles, making papers appear for keyword searches the articles were not relevant for Google Scholar indexed some nonsensical articles we randomly created with the paper generator SciGen and Google Scholar linked to manipulated versions of research papers that contained a Viagra advertisement. To find out whether these concerns are justified, we conducted several tests on Google Scholar. Some were concerned researchers could use our guidelines to manipulate rankings of scientific articles and promote what we call ‗academic search engine spam‘. Feedback in the academic community to these guidelines was diverse. In a previous paper we provided guidelines for scholars on optimizing research articles for academic search engines such as Google Scholar. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |