An Agricultural Product Recommendation Algorithm Based on Fusion Representation - HUANG Yinglai; JI Yuchao; LIU Zhenbo (Journal of Harbin University of Science and Technology, 2024)
This paper proposes a kind of recommendation algorithm for agricultural commodities with fusion representation, in response to the issue of unexpected results on agricultural product e-commerce platforms due to the strong seasonality and regionality of products, as well as the variable user behaviors. Firstly, it integrates Long Short-Term Memory Networks and Attention Network to make up Deep Interest Network. This step aims to catch the potential feature of the item. Secondly, it builds up user-product bipartite graph. Then, it uses Graph Neural Network to abstract the impacts that connection information of graph data has on each node. And it also updates the embedded presentation of the node to catch the potential feature of user. Last, the two potential features are fed into a Multilayer Perceptron to get the order rate of the to-be-recommended agricultural commodities. This step combines the user ′ s deep interests derived from their behavior sequence with deep interest network to generate personalized recommendations. The results of experiment have shown that, compared with the previous model, the AUC target of recommendation algorithm for agricultural commodities with fusion representation has increased over 9% . Compared with the situation without taking the embedded presentation of the node into consideration, the AUC, Accuracy and Recall have all increased.