  With the rapid development of information technology, "information overload"
has become the main theme that plagues people's online life. As an effective
tool to help users quickly search for useful information, a personalized
recommendation is more and more popular among people. In order to solve the
sparsity problem of the traditional matrix factorization algorithm and the
problem of low utilization of review document information, this paper proposes
a Bicon-vMF algorithm based on improved ConvMF. This algorithm uses two
parallel convolutional neural networks to extract deep features from the user
review set and item review set respectively and fuses these features into the
decomposition of the rating matrix, so as to construct the user latent model
and the item latent model more accurately. The experimental results show that
compared with traditional recommendation algorithms like PMF, ConvMF, and
DeepCoNN, the method proposed in this paper has lower prediction error and can
achieve a better recommendation effect. Specifically, compared with the
previous three algorithms, the prediction errors of the algorithm proposed in
this paper are reduced by 45.8%, 16.6%, and 34.9%, respectively.
