中国农业大学学报2025，Vol.30Issue(7)：218-229,12.DOI:10.11841/j.issn.1007-4333.2025.07.19
基于深度学习的动植物新品种精准推荐方法
An accurate recommendation method for new animal and plant varieties based on deep learning
摘要
Abstract
In response to the problems of information asymmetry,untimely delayed updates and difficulty in implementing information services for new animal and plant species resources in China,a two-step recommendation algorithm that combines deep neural networks and attention mechanisms is proposed in this study.Firstly,based on the full-text data of animal and plant species resources,the algorithm constructs a vocabulary library of animal and plant species,and builds a Siamese BERT(Bidirectional Encoder Representations from Transformers)network.BERT was used to obtain text context-related semantic features.The bidirectional maximum matching algorithm is used to obtain variety dictionary feature vectors.Integrates semantic and dictionary features,a regression model was trained through similarity calculation to achieve the similarity judgment between new animal and plant varieties and historical varieties.Next,by integrating factors influencing variety recommendation,such as user static attribute characteristics,user behavior characteristics,and animal and plant variety resource characteristics,the industry differences and habits of users in the agricultural field are comprehensively analyzed.A user feature and variety feature representation method is proposed for animal and plant variety recommendation,and a user interest network model is constructed based on Convolutional Neural Network(CNN)and the attention mechanism is introduced to achieve accurate matching and recommendation between users and varieties.By monitoring new variety resources in real time on the internet,fully automatic recommendation of new animal and plant varieties is realized.The experimental performance testing and effectiveness verification results show that the algorithm achieves the accuracy and F1 score evaluation indicators of 84.1％and 0.832,respectively.Compared with the traditional recommendation algorithms based on collaborative filtering and matrix decomposition,the algorithm proposed in this study can more accurately recommend new varieties of animals and plants.关键词
动植物品种资源/推荐/深度神经网络/两步推荐/兴趣模型Key words
animal and plant variety resources/recommend/deep neural network/two-step recommendation/interest model分类
信息技术与安全科学引用本文复制引用
顾静秋,郭旺,朱华吉,郝鹏,吴华瑞..基于深度学习的动植物新品种精准推荐方法[J].中国农业大学学报,2025,30(7):218-229,12.基金项目
科技创新2030重大项目子课题(2022ZD0115705-05) （2022ZD0115705-05）
