论文标题
通过实例群集为推荐系统选择推荐系统的均值算法
Per-Instance Algorithm Selection for Recommender Systems via Instance Clustering
论文作者
论文摘要
如果用户，建议上下文，应用程序和用户界面略有不同，建议算法的性能有所不同。在其他领域（例如组合问题解决）中类似地观察到，算法在每个实例中的性能都不同。在这些领域，元学习成功地用于预测每个实例的最佳算法，以提高整体系统性能。到目前为止，对于推荐系统，每种算法的选择未成功。在本文中，我们提出了一个元素元学习者，该元学习群将数据实例簇起来，并根据集群成员资格预测了看不见的实例的最佳算法。我们使用10种协作和4种基于内容的过滤算法，用于改变聚类参数的方法测试我们的方法，并在Alpha = 0.053（MAE：0.7107 vs lightGBM 0.7214; t-tsest）中找到对最佳性能基础算法的显着改善。我们还在评分数据集上探索了基本算法的性能，并从经验上表明，完美算法选择器的误差会单调地减少较大的算法池。据我们所知，这是在推荐系统中选择第一种有效的元学习技术。
Recommendation algorithms perform differently if the users, recommendation contexts, applications, and user interfaces vary even slightly. It is similarly observed in other fields, such as combinatorial problem solving, that algorithms perform differently for each instance presented. In those fields, meta-learning is successfully used to predict an optimal algorithm for each instance, to improve overall system performance. Per-instance algorithm selection has thus far been unsuccessful for recommender systems. In this paper we propose a per-instance meta-learner that clusters data instances and predicts the best algorithm for unseen instances according to cluster membership. We test our approach using 10 collaborative- and 4 content-based filtering algorithms, for varying clustering parameters, and find a significant improvement over the best performing base algorithm at alpha=0.053 (MAE: 0.7107 vs LightGBM 0.7214; t-test). We also explore the performances of our base algorithms on a ratings dataset and empirically show that the error of a perfect algorithm selector monotonically decreases for larger pools of algorithm. To the best of our knowledge, this is the first effective meta-learning technique for per-instance algorithm selection in recommender systems.
