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In this study, we present a novel clustering-based collaborative filtering(CF) method for recommender systems. Clustering-based CF methods caneffectively deal with data sparsity and scalability problems. However, most ofthem are applied to a single representation space, which might not characterizecomplex user-item interactions well. We argue that the user-item interactionsshould be observed from multiple views and characterized in an adaptive way. Toaddress this issue, we leveraged the global and local properties to constructmultiple representation spaces by learning various training datasets and lossfunctions. An attention network was built to generate a blended representationaccording to the relative importance of the representation spaces for eachuser-item pair, providing a flexible way to characterize diverse user-iteminteractions. Substantial experiments were evaluated on four popular benchmarkdatasets. The results show that the proposed method is superior to several CFmethods where only one representation space is considered.

Attention on Global-Local Representation Spaces in Recommender Systems