@@ -8,7 +8,7 @@ Compared to the listed work, it is not unknown that in some subject areas \texti
However, the work published by \citet{Rendle19} also clearly stands out from the above-mentioned work. In contrast to them, not only the problem for the \textit{MovieLens10M-dataset} in combination with \textit{matrix-factorization} is recognized. Rather, the problem is brought one level higher. Thus, it succeeds in gaining a global and reflected but still distanced view of the \textit{best practice} in the field of \textit{recommender systems}.
Besides calling for \textit{uniform standards}, \citet{Rendle19} criticizes the way the \textit{scientific community} thinks. \citet{Rendle19} recognizes the \textit{publication-bias} addressed by \citet{Sterling59}. The so-called \textit{publication-bias} describes the problem that there is a \textit{statistical distortion} of the data situation within a \textit{scientific topic area}, since only successful or modern papers are published. \citet{Rendle19} clearly abstracts this problem from the presented experiment. The authors see the problem in the fact that a scientific paper is subject to a \textit{pressure to perform} which is based on the \textit{novelty} of such a paper. This thought can be transferred to the \textit{file-drawer-problem} described by \citet{Rosenthal79}. This describes the problem that many \textit{scientists} do not publish their work and, out of concern about not meeting the \textit{publication standards} such as \textit{novelty} or the question of the \textit{impact on the community}, do not submit their results at all and prefer to \textit{keep them in a drawer}. Although the problems mentioned above are not directly addressed, they can be abstracted due to the detailed presentation. In contrast to the other works, this way a wanted or unwanted abstraction and naming of concrete and comprehensible problems is achieved.
Nevertheless, criticism must also be made of the work published by \citet{Rendle19}. Despite the high standard of the work, it must be said that the problems mentioned above can be identified but are not directly addressed by the authors. The work of \citet{Rendle19} even lacks an embedding in the context above. Thus, the experienced reader who is familiar with the problems addressed by \citet{Armstrong09}, \citet{Sterling59} and \citet{Rosenthal79} becomes aware of the contextual and historical embedding and value of the work. In contrast, \citet{Lin19} and \citet{Dacrema19}, published in the same period, succeed in this embedding in the contextual problem and in the previous work. Moreover, it is questionable whether the problem addressed can actually lead to a change in \textit{long-established thinking}. Especially if one takes into account that many scientists are also investigating the \textit{transferability} of new methods to the \textit{recommender problem}. Thus, the call for research into \textit{better baselines} must be viewed from two perspectives. On the one hand, it must be noted that \textit{too weak baselines} can lead to a false understanding of new methods. On the other hand, it must also be noted that this could merely trigger the numerical evaluation in a competitive process to find the best method, as was it the case with the \textit{Netflix-Prize}. However, in the spirit of \citet{Sculley18}, it should always be remembered that: \textit{"the goal of science is not wins, but knowledge"}.
Nevertheless, criticism must also be made of the work published by \citet{Rendle19}. Despite the high standard of the work, it must be said that the problems mentioned above can be identified but are not directly addressed by the authors. The work of \citet{Rendle19} even lacks an embedding in the context above. Thus, the experienced reader who is familiar with the problems addressed by \citet{Armstrong09}, \citet{Sterling59} and \citet{Rosenthal79} becomes aware of the contextual and historical embedding and value of the work. In contrast, \citet{Lin19} and \citet{Dacrema19}, published in the same period, succeed in this embedding in the contextual problem and in the previous work. Moreover, it is questionable whether the problem addressed can actually lead to a change in \textit{long-established thinking}. Especially if one takes into account that many scientists are also investigating the \textit{transferability} of new methods to the \textit{recommender problem}. Thus, the call for research into \textit{better baselines} must be viewed from two perspectives. On the one hand, it must be noted that \textit{too weak baselines} can lead to a false understanding of new methods. On the other hand, it must also be noted that this could merely trigger the numerical evaluation in a competitive process to find the best method, as it was the case with the \textit{Netflix-Prize}. However, in the spirit of \citet{Sculley18}, it should always be remembered that: \textit{"the goal of science is not wins, but knowledge"}.
As the authors \citet{Rendle} and \citet{Koren} were significantly \textit{involved} in this competition, the points mentioned above are convincing by the experience they have gained. With their results they support the very simple but not trivial statement that finding good \textit{baselines} requires an \textit{immense effort} and this has to be \textit{promoted} much more in a \textit{scientific context}. This implies a change in the \textit{long-established thinking} about the evaluation of scientific work. At this point it is questionable whether it is possible to change existing thinking. This should be considered especially because the scientific sector, unlike the industrial sector, cannot provide financial motivation due to limited resources. On the other hand, it must be considered that the individual focus of a work must also be taken into account. Thus, it is \textit{questionable} whether the \textit{scientific sector} is able to create such a large unit with regard to a \textit{common goal} as \textit{Netflix} did during the competition.
It should be clearly emphasized that it is immensely important to use sharp \textit{baselines} as guidelines. However, in a \textit{scientific context} the \textit{goal} is not as \textit{precisely defined} as it was in the \textit{Netflix-Prize}. Rather, a large part of the work is aimed at investigating whether new methods such as \textit{neural networks} etc. are applicable to the \textit{recommender problem}.
