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.

翻译：推荐算法的数量持续增长。研究者们不断提出新方法，并将其与现有模型（称为基线）进行比较。由于推荐模型种类繁多，在论文中难以评估应选择哪些算法。为解决此问题，我们收集并发布了一个包含903篇论文中使用的推荐模型（既包括作为基线的模型，也包括作为提出方法的模型）信息的数据集。该数据集可被视为一个包含论文与先前提出模型之间交互的典型数据集。此外，我们对该数据集进行了描述性分析，并指出了可通过该数据探索的潜在挑战。进一步地，我们基于已建立的方法论开展了大量实验，以构建适用于该数据集的高效推荐算法。实验结果表明，选择最合适的基线以提出新的推荐方法这一任务，可通过现有最先进的协同过滤模型进行考量并成功解决。最后，我们讨论了局限性及未来工作方向。