RecBaselines2023: a new dataset for choosing baselines for recommender models

TL;DR

This paper introduces RecBaselines2023, a comprehensive dataset of recommender models from 903 papers, enabling better baseline selection for developing new recommender algorithms, and demonstrates the effectiveness of collaborative filtering in this task.

Contribution

The paper presents a new dataset of recommender models from academic papers and shows how collaborative filtering can effectively select baselines.

Findings

Collaborative filtering models can successfully recommend suitable baselines.

The dataset contains detailed information on 903 papers and their models.

Analysis highlights challenges and future directions for baseline selection.

Abstract

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.