  The number of proposed recommender algorithms continues to grow. The authors
propose new approaches and compare them with existing models, called baselines.
Due to the large number of recommender models, it is difficult to estimate
which algorithms to choose in the article. To solve this problem, we have
collected and published a dataset containing information about the recommender
models used in 903 papers, both as baselines and as proposed approaches. This
dataset can be seen as a typical dataset with interactions between papers and
previously proposed models. In addition, we provide a descriptive analysis of
the dataset and highlight possible challenges to be investigated with the data.
Furthermore, we have conducted extensive experiments using a well-established
methodology to build a good recommender algorithm under the dataset. Our
experiments show that the selection of the best baselines for proposing new
recommender approaches can be considered and successfully solved by existing
state-of-the-art collaborative filtering models. Finally, we discuss
limitations and future work.
