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Commit e087fd29 authored by Marc Feger's avatar Marc Feger
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{\Large Department of Computer Science\\}
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Data \& Knowledge Engineering\\
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{\small Universitätsstr. 1 \hspace{5ex} D--40225 Düsseldorf\\}
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{\Large Submission}
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\bearbeiter
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\section{Introduction}
Today's use of \textit{recommender systems} finds an increased and yet unconscious access to our everyday life.
More and more areas of life are therefore subject to constant optimisation.
Companies such as \textit{Netflix}, \textit{Amazon} and \textit{YouTube} adapt their product proposals to the individual wishes of their customers.
To make this possible, the various \textit{collaborative-filtering} and \textit{content-based} \textit{recommender systems} are used.
Since \citet{JuKa90} first presented \textit{recommender systems} as a kind of intelligent bookcase, much effort has been put into the development and research of such systems.
The most diverse subject areas were not only illuminated by the industry.
A whole new branch of research also opened up for science.
In their work ``On the Diffculty of Evaluating Baselines A Study on Recommender Systems`` \citet{Rendle19} show that current research on the \textit{MovieLens10M} dataset leads in a wrong direction.
In addition to general problems, they particulary list wrong working methods and missunderstood \textit{baselines} by breaking them by a number of simple methods such as \textit{matrix-factorization}.
They were able to beat the existing baselines by not taking them for granted.
On the contrary, they questioned them and transferred well evaluated and understood properties of the baselines from the \textit{Netflix-Challenge} to them.
As a result, they were not only able to beat the \textit{baselines} reported for the \textit{MovieLens10M}, but also the newer methods from the last 5 years of research. Therefore, it can be assumed that the current and former results obtained on the \textit{MovieLens10M} dataset were not sufficient to be considered as a true baseline. Thus they show the community a critical error on which can be found not only in the evaluation of \textit{recommender systems} but also in other scientific areas.
As a first problem, the authors point out that scientific papers whose focus is on better understanding and improving existing \textit{baselines} do not receive recognition because they do not seem innovative enough. In contrast to industry, which tenders horrendous prizes for researching and improving such \textit{baselines}, there is a lack of such motivation in the scientific field. From the authors point of view, the scientific work on the \textit{MovieLens10M} dataset is misdirected, because one-off evaluations leading to one-hit-wonders, which are then used as a starting point for further work. Thus \citet{Rendle19} points out as a second point of criticism that the need for further basic research for the \textit{MovieLens10M} dataset is not yet exhausted.
This submission takes a critical look at the topic presented by \citet{Rendle19}. In addition, basic terms and the results obtained are presented in a way that is comprehensible to the non-experienced reader.
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@book{JuKa90,
author = {Karlgren, Jussi},
institution = {KTH, Computer and Systems Sciences, DSV},
institution = {Stockholm University},
publisher = {Department of Computer and Systems Sciences, Stockholm University},
title = {An algebra for recommendations : Using reader data as a basis for measuring document proximity},
series = {SYSLAB technical reports},
number = {179},
pages = {1--11},
abstract = {A measure for proximity between documents is defined, based on data from readers. This proximity measure can be further investigated as a tool document retrieval, and as to provide data for concept formation experiments. },
year = {1990}
}
@article{Rendle19,
author = {Steffen Rendle and
Li Zhang and
Yehuda Koren},
title = {On the Difficulty of Evaluating Baselines: {A} Study on Recommender
Systems},
journal = {CoRR},
volume = {abs/1905.01395},
year = {2019},
url = {http://arxiv.org/abs/1905.01395},
archivePrefix = {arXiv},
eprint = {1905.01395},
timestamp = {Mon, 27 May 2019 13:15:00 +0200},
biburl = {https://dblp.org/rec/bib/journals/corr/abs-1905-01395},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
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