Utilizing Metadata to Select a Recommendation Algorithm for a User or an Item
Utilizing Metadata to Select a Recommendation Algorithm for a User or an Item
复制标题
利用元数据为用户或项目选择推荐算法
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
V. Meltsov
中科院分区：
文献类型：
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作者：
Alexander Nechaev;N. Zhukova;V. Meltsov
.一般来说，推荐系统通过帮助服务的用户找到他们感兴趣的项目来解决信息过载的问题。有很多算法和模型可用于构建这样的项目子集，它们的性能不仅可能因不同的数据集而异，而且可能因单个数据集的不同部分而异。这个问题导致了“算法选择问题”。这个问题的许多最先进的解决方案都基于元学习。它将任务特征与基本算法和模型的性能相关联。本文提出了一种针对特定用户（或项目）的推荐算法选择方法，该方法使用用户（或项目）的显式元数据和可计算统计元特征的二进制表示。在所提出的方法中有两种不同的技术，它们分别基于这种二进制数据的分类或聚类。元学习过程几乎是自动化的。实验结果表明，该方法用于推荐算法的选择是合理有效的。在大多数情况下，使用元模型的推荐系统显示出较低的评级预测错误相比，任何其他使用单一的模型或算法的所有用户（或项目），而在少数测试中，他们的表现是一样的。对评估结果的详细分析允许确认所描述的元模型可以用于现实世界的系统中，以改善特定用户的体验。
. In general, recommender systems solve the problem of information overload by helping users of services to find items in which they are interested. There are plenty of algorithms and models that can be used to build such a subset of items, and their performance may vary not only for different datasets but for separate parts of a single one. This issue leads to the ”algorithm selection problem”. Many state-of-art solutions to this problem are based on meta-learning. It associates the task features with the performance of the base-level algorithms and models. This paper presents the method of recommendation algorithm selection for particular users (or items) that uses binary representations of both explicit metadata of them and computable statistical meta-features. There are two different techniques within the proposed method, which are based on classification or clustering of such binary data, respectively. The meta-learning process is almost automated. The findings of the experiments prove that the usage of the method for recommendation algorithm selection is reasonable and effective. In most cases, a recommender system that uses the metamodel shows lower rating prediction errors compared to any other one utilizing a single model or algorithm for all the users (or items), while in a small number of tests their performance is just the same. The detailed analysis of the evaluation results allows for affirm-ing that the described metamodels can be used in real-world systems to improve the experience of particular users.