  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 representation space, which might not characterize
complex user-item interactions well. We argue that the user-item interactions
should be observed from multiple views and characterized in an adaptive way. To
address this issue, we leveraged the global and local properties to construct
multiple representation spaces by learning various training datasets and loss
functions. An attention network was built to generate a blended representation
according to the relative importance of the representation spaces for each
user-item pair, providing a flexible way to characterize diverse user-item
interactions. Substantial experiments were evaluated on four popular benchmark
datasets. The results show that the proposed method is superior to several CF
methods where only one representation space is considered.
