Research And Development Of Agricultural Product E-commerce Recommendation System Based On NCRF++ Model

With the popularity of e-commerce,more and more people have set consumption scenarios on e-commerce platforms.Agricultural products are a popular commodity category in the e-commerce platform.A suitable recommendation algorithm can more effectively recommend the agricultural products of interest to users.However,the current agricultural product e-commerce recommendation system still has the problem of failing to fully explore the user's comment information and user's emotional tendency.In order to solve the above-mentioned problems,this article studies the sentiment classification of user reviews,and on the basis of this research combines user review preferences to improve existing recommendation algorithms to implement agricultural product e-commerce recommendation systems.The following are the specific research directions and contents:(1)In view of the domain characteristics of agricultural product e-commerce reviews and the problem that the classification of ordinary sentiment dictionaries is not accurate,this paper proposes a research method of e-commerce review sentiment classification based on domain sentiment dictionary and NCRF++ model.First,the feasibility of NCRF++ model in word segmentation technology is verified.At the same time,the domain characteristics of agricultural product e-commerce are considered,and the sentiment dictionary of agricultural product e-commerce review domain is constructed based on the existing sentiment dictionary,which verifies the applicability of domain sentiment dictionary for sentiment classification.Finally,the domain sentiment dictionary is introduced into the BERT model to ensure the accuracy of the classification of user comments on sentiment tendencies.The experimental results show that introducing the domain sentiment dictionary into the BERT model can effectively improve the model's effect on the sentiment classification of user comments.(2)In view of the current common collaborative filtering algorithms failing to dig into the preference information in user reviews,this paper proposes a recommendation algorithm based on the user review sentiment model on the basis of identifying the sentiment tendency set of user reviews,and mines the feature attributes of items in user reviews Preferences,build user comment sentiment models.Using the improved Euclidean distance similarity algorithm,the user comment sentiment model matrix is used instead of the traditional user score to calculate the similarity,to ensure that the target user's neighbor set is found to be more accurate.The experimental results show that replacing the user-sentence sentiment model with scores is more conducive to searching for neighbor sets,and can improve the accuracy,recall,and F1 value of items recommended to target users.(3)Design and implement domain-based sentiment dictionary classification method and user comment sentiment model recommendation algorithm,and apply to e-commerce agricultural product recommendation system.The application results show that the application of the recommendation algorithm in this article provides a smoother interactive experience for agricultural product e-commerce users,and the recommendation results are more accurate and effective.