Design And Implementation Of Fruit E-commerce System Based On Content Recommendatio

With the rapid development of Internet technology,there is an increasing demand for personalized recommendation systems.Addressing the issues of cold start and lack of personalization in traditional recommendation systems,content-based recommendation algorithms have been widely applied.This study aims to research and implement a content-based fruit recommendation platform that achieves personalized recommendations between users and fruits by recording user interactions.In this study,the pulse control algorithm is employed to calculate the similarity between users and products,enabling personalized recommendations.Key steps,including feature extraction,feature vector representation,and similarity calculation,are conducted to establish the feature and product libraries of the fruit recommendation platform.Administrators can upload features and products to build the platform’s feature library,while users provide data support for personalized recommendations through their recorded interactions.The platform recommends fruits that align with users’ interests by utilizing similarity calculations.Additionally,a frontend-backend separation architecture is adopted,utilizing the Spring Boot framework to build the backend API and the Vue framework to construct the frontend interface.The platform’s main functionalities include feature and product uploading,user interaction recording,and fruit recommendations.Through experiments and testing,the effectiveness and feasibility of the content-based recommendation algorithm in the fruit recommendation domain are demonstrated.User evaluations indicate that the recommendation system can provide highly personalized fruit recommendations,thereby enhancing user shopping satisfaction.In conclusion,the content-based fruit recommendation platform holds significant potential and development prospects,offering users more personalized fruit recommendation services and supporting businesses in increasing sales and market competitiveness.Further research and improvements in content-based recommendation algorithms,incorporating additional user features and product attributes,will enhance the system’s performance and user experience.