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Recommending Collaborative Filtering Algorithms Using Subsampling Landmarkers

机译：推荐使用二次采样地标的协作过滤算法

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摘要

Recommender Systems have become increasingly popular, propelling the emergence of several algorithms. As the number of algorithms grows, the selection of the most suitable algorithm for a new task becomes more complex. The development of new Recommender Systems would benefit from tools to support the selection of the most suitable algorithm. Metalearning has been used for similar purposes in other tasks, such as classification and regression. It learns predictive models to map characteristics of a dataset with the predictive performance obtained by a set of algorithms. For such, different types of characteristics have been proposed: statistical and/or information-theoretical, model-based and landmarkers. Recent studies argue that landmarkers are successful in selecting algorithms for different tasks. We propose a set of landmarkers for a Metalearning approach to the selection of Collaborative Filtering algorithms. The performance is compared with a state of the art systematic metafeatures approach using statistical and/or information-theoretical metafeatures. The results show that the metalevel accuracy performance using landmarkers is not statistically significantly better than the metafeatures obtained with a more traditional approach. Furthermore, the baselevel results obtained with the algorithms recommended using landmarkers are worse than the ones obtained with the other metafeatures. In summary, our results show that, contrary to the results obtained in other tasks, these landmarkers are not necessarily the best metafeatures for algorithm selection in Collaborative Filtering.
机译：推荐系统已变得越来越流行，推动了几种算法的出现。随着算法数量的增加，为新任务选择最合适的算法变得更加复杂。新的推荐系统的开发将受益于支持选择最合适算法的工具。在其他任务（例如分类和回归）中，金属收入已用于类似目的。它学习预测模型，以通过一组算法获得的预测性能来映射数据集的特征。为此，已经提出了不同类型的特征：统计和/或信息理论，基于模型和标志物。最近的研究认为，标志性人物成功地选择了用于不同任务的算法。我们为选择协作过滤算法的Metalearning方法提出了一组标记。使用统计和/或信息理论的元功能将性能与最先进的系统元功能方法进行比较。结果表明，使用地标笔进行的元级准确度性能在统计上并不比使用传统方法获得的元功能好。此外，使用推荐使用地标的算法获得的基本水平结果要比使用其他元功能获得的结果差。总而言之，我们的结果表明，与其他任务中获得的结果相反，这些标志物不一定是协作过滤中算法选择的最佳元功能。

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