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The number of proposed recommender algorithms continues to grow. The authorspropose new approaches and compare them with existing models, called baselines.Due to the large number of recommender models, it is difficult to estimatewhich algorithms to choose in the article. To solve this problem, we havecollected and published a dataset containing information about the recommendermodels used in 903 papers, both as baselines and as proposed approaches. Thisdataset can be seen as a typical dataset with interactions between papers andpreviously proposed models. In addition, we provide a descriptive analysis ofthe dataset and highlight possible challenges to be investigated with the data.Furthermore, we have conducted extensive experiments using a well-establishedmethodology to build a good recommender algorithm under the dataset. Ourexperiments show that the selection of the best baselines for proposing newrecommender approaches can be considered and successfully solved by existingstate-of-the-art collaborative filtering models. Finally, we discusslimitations and future work.

RecBaselines2023: a new dataset for choosing baselines for recommender models