Research on Commodity Recommendation System Based on Deep Learning

Abstract

The traditional matrix factorization model cannot effectively extract the features of users and items, but the feature information can be extracted well based on the deep learning model. At present, the mainstream recommendation algorithms based on deep learning only make recommendation prediction in the form of the product of neural network output or item features and user features, and cannot fully mine the relationship between users and items. Based on this, this paper proposes a recommendation algorithm based on the combination of text convolution neural network and singular value decomposition (Bias SVD) with biased terms. The text convolution neural network (Text CNN) is used to fully extract the feature information of users and items, and then the singular value decomposition method is used to make recommendations to deeply understand the document context information and further improve the accuracy of recommendation. The algorithm is widely evaluated and analyzed on two real data sets of MovieLens, and the accuracy of recommendation is obviously better than that of ConvMF algorithm and mainstream deep learning recommendation algorithm.