Research On Personalized Recommendation Of Agricultural Products Based On Sentiment Analysis Of Online Reviews

In recent years,with the rapid development of e-commerce,buying agricultural products online has been deeply loved by consumers as a way of shopping.However,due to the wide variety of agricultural products on e-commerce platforms,users need to spend more time and energy to find their favorite products,which affects the shopping experience.Therefore,for e-commerce platforms,targeted recommendations are very important according to consumers' preferences.Traditional agricultural product recommendation is mostly based on the user's rating of the purchased product to infer user interest,thereby making recommendations,but ignores the impact of product online reviews on the recommendation effect.Online reviews contain a large number of users' personal preferences for product features,which are extremely important for personalized recommendations.In view of this,this article analyzes the online reviews of well-known domestic e-commerce platforms,takes Gansu Province's characteristic agricultural products as the research object,and constructs a personalized recommendation model based on sentiment analysis.The model is based on sentiment analysis,using matrix decomposition,improved Bhattacharyya coefficient similarity and hybrid collaborative filtering algorithm to improve the traditional collaborative filtering algorithm,and then improve the accuracy of recommendation.In summary,the main work of this article is as follows:(1)The sentiment dictionary in the field of agricultural products is expanded,and the sentiment score matrix of user reviews is calculated based on the sentiment dictionary.On the basis of the existing sentiment dictionary,the sentiment dictionary in the field of agricultural products is expanded by using the sentiment point mutual information algorithm and the LDA topic model,and the network word dictionary is added to improve it,so as to calculate the sentiment value of the user's comment,and then obtain sentiment score matrix.(2)A hybrid recommendation algorithm combining matrix decomposition and improved Bhattacharyya coefficient is constructed.Firstly,use the bias-based matrix factorization algorithm BMF to fill in the missing values of the score matrix;secondly,improve the traditional Bhattacharyya coefficient similarity measurement method;thirdly,integrate the two collaborative filtering recommendation algorithms based on user and item;Finally,the BMF algorithm,improved Bhattacharyya coefficient and hybrid collaborative filtering algorithm are combined to obtain a hybrid recommendation algorithm,and the accuracy of the algorithm is verified on the Movie Lens data set.(3)A personalized recommendation model of agricultural products based on sentiment analysis is constructed.Based on the sentiment analysis of the dictionary,the sentiment score matrix is obtained.On this basis,a hybrid recommendation algorithm is combined to establish a personalized recommendation model based on sentiment analysis,and the superiority of the model is verified by comparison with UCF,ICF,and HCF three recommendation models.(4)A personalized recommendation system for agricultural products is designed.The personalized recommendation model based on sentiment analysis is used in the recommendation system to provide users with personalized recommendation of agricultural products.