Abstract: In order to solve the problem of poor accuracy in traditional recommendation, a multi-layer K-nearest neighbor (KNN) network recommend model KNN-GWD, in combination of graph neural network (GNN) and wide & deep network was constructed. In the model, the KNN classification method is for data noise filtering to improve data quality. GNN is used to extract the node embedding representation of user's conversation graphs, and capture user's short-term interest by weighting user's global characteristics based on attention mechanism. Wide&Deep is used to solve the problem of model overgeneralization in the case of sparse data. In order to verify the effectiveness of the model, comparative experiments were carried out on MovieLens-1M, Bing-News and Book-Crossing data sets with this model and six other traditional recommendation methods. Experimental results show that the evaluation indicators of this model are better. In order to further verify the feasibility of the proposed model in the actual application field, an agricultural integrated management App fertilizer recommendation system was built with the accuracy of recommended results of 0.721 and the area under curve of 0.784, which met the expected application requirements.

Key words: recommendation system, feature representation, user preference, agricultural field