In this study, we present a novel clustering-based collaborative filtering (CF) method for recommender systems. Clustering-based CF methods can effectively deal with data sparsity and scalability problems. However, most of them are applied to a single embedding space, which might not characterize complex user-item interactions well. We argue that user-item interactions should be observed from multiple views and characterized in an adaptive way. To address this issue, we leveraged the relation between global space and local clusters to construct multiple embedding spaces by learning variant training datasets and loss functions. An attention model was then built to provide a dynamic blended representation according to the relative importance of the embedding spaces for each user-item pair, forming a flexible measure to characterize variant user-item interactions. Substantial experiments were performed and evaluated on four popular benchmark datasets. The results show that the proposed method is effective and competitive compared to several CF methods where only one embedding space is considered.

中文翻译：

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注意推荐系统中的全局局部嵌入空间

在这项研究中，我们提出了一种用于推荐系统的新颖的基于聚类的协同过滤（CF）方法。基于集群的CF方法可以有效地处理数据稀疏性和可伸缩性问题。但是，它们中的大多数都应用于单个嵌入空间，这可能无法很好地描述复杂的用户项交互。我们认为，应该从多个角度观察用户-项目交互，并以自适应方式对其进行特征化。为了解决这个问题，我们通过学习变量训练数据集和损失函数，利用全局空间和局部集群之间的关系来构造多个嵌入空间。然后建立注意力模型，以根据每个用户项目对的嵌入空间的相对重要性提供动态混合表示，形成一种灵活的方法来表征各种用户项目交互。在四个流行的基准数据集上进行了大量的实验并进行了评估。结果表明，与仅考虑一个嵌入空间的几种CF方法相比，该方法是有效且具有竞争力的。