  In general, recommendation can be viewed as a matching problem, i.e., match
proper items for proper users. However, due to the huge semantic gap between
users and items, it's almost impossible to directly match users and items in
their initial representation spaces. To solve this problem, many methods have
been 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-based CF methods try to map users and items
into a common representation space. In this case, the higher similarity between
a user and an item in that space implies they match better. Matching function
learning-based CF methods try 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 limited
expressiveness of dot product and the weakness in capturing low-rank relations
respectively. To this end, we propose a general framework named DeepCF, short
for Deep Collaborative Filtering, to combine the strengths of the two types of
methods and overcome such flaws. Extensive experiments on four publicly
available datasets demonstrate the effectiveness of the proposed DeepCF
framework.
