Abstract
Inspired by the significant success of deep learning,
some attempts have been made to introduce deep
neural networks (DNNs) in recommendation systems to learn users’ preferences for items. Since
DNNs are well suitable for representation learning, they enable recommendation systems to generate more accurate prediction. However, they inevitably result in high computational and storage
costs. Worse still, due to the relatively small number of ratings that can be fed into DNNs, they may
easily lead to over-fitting. To tackle these problems, we propose a novel recommendation algorithm based on Back Propagation (BP) neural network with Attention Mechanism (BPAM). In particular, the BP neural network is utilized to learn the
complex relationship of the target users and their
neighbors. Compared with deep neural network,
the shallow neural network, i.e., BP neural network,
can not only reduce the computational and storage
costs, but also prevent the model from over-fitting.
In addition, an attention mechanism is designed to
capture the global impact on all nearest target users for each user. Extensive experiments on eight
benchmark datasets have been conducted to evaluate the effectiveness of the proposed model