  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.
