  Collaborative Filtering (CF) based recommendation methods have been widely
studied, which can be generally categorized into two types, i.e.,
representation learning-based CF methods and matching function learning-based
CF methods. Representation learning tries to learn a common low dimensional
space for the representations of users and items. In this case, a user and item
match better if they have higher similarity in that common space. Matching
function learning tries to directly learn the complex matching function that
maps user-item pairs to matching scores. Although both methods are well
developed, they suffer from two fundamental flaws, i.e., the representation
learning resorts to applying a dot product which has limited expressiveness on
the latent features of users and items, while the matching function learning
has weakness in capturing low-rank relations. To overcome such flaws, we
propose a novel recommendation model named Balanced Collaborative Filtering
Network (BCFNet), which has the strengths of the two types of methods. In
addition, an attention mechanism is designed to better capture the hidden
information within implicit feedback and strengthen the learning ability of the
neural network. Furthermore, a balance module is designed to alleviate the
over-fitting issue in DNNs. Extensive experiments on eight real-world datasets
demonstrate the effectiveness of the proposed model.
