Comment text-oriented graph neural network recommendation method

The invention discloses a comment text-oriented graph neural network recommendation method, which comprises the following steps of: 1, constructing a bipartite graph of a user and a commodity by using comments and scores of the user on the product, including a user node set, a product node set, a score matrix and a comment feature tensor; 2, constructing a comment text-oriented graph convolution method, taking comments and scores as edge features to participate in graph convolution, and coding user and product characterization; and 3, enhancing the characterization of the user and the product by utilizing graph contrast learning. 4, constructing an interaction layer, and coding an interaction vector from the representation of the user and the product; and 5, drawing close the distribution of the interactive representation and the comment vector through comparative learning. And 6, predicting a score according to the interactive characterization so as to realize product recommendation. According to the method, comments and scores are used as interaction features of the user and the product, the mutual influence between the comments and the scores is adaptively learned, more accurate node characterization and finer user preference are learned, and therefore the recommendation performance can be improved.