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

Bi-convolution matrix factorization algorithm based on improved ConvMF