Research On The Attractiveness Of Food Takeaway Images Based On Machine Learnin

With its convenience and diversity,food delivery is playing an increasingly important role in modern society.As technology and services continue to upgrade,the food delivery industry continues to grow.In the take-out industry,beautiful food images have become one of the important means for businesses to attract consumers.Takeaway images can intuitively show the appearance and taste of dishes,which has a great influence on consumers’ choice of takeaway platforms and dishes.By analyzing the attractiveness of takeaway images,enterprises can understand consumers’ preferences and expectations for dishes and provide better services according to these preferences and expectations,thus improving consumers’ information behavior.Then enterprises can also understand the dishes,food placement and photography more attractive consumers,so as to optimize marketing strategy,improve sales.Therefore,it is of great significance to analyze the impact of takeaway food images on user attraction.This research focuses on the relationship between the attractiveness of food takeaway image and its visual elements,and proposes the establishment of an automatic recognition of food takeaway image attractiveness.Specifically,in the research process,user annotation is used to objectify the subjective evaluation,feature engineering is used to construct the features of food images and machine learning is used for supervised learning to predict the attractiveness value of food images.By analyzing the labeling results and the machine learning model prediction results,this study found that the color,structure,arrangement and other factors of food images can affect people’s attraction to them.In addition,by analyzing the data,this study also discussed the factors that affect the attractiveness of food images.In fact,the features extracted in the deep neural network cannot be controlled by researchers or users,so it is impossible to make clear the relevant information in the picture corresponding to a certain feature.In order to provide clear guidance for take-out businesses when using pictures,we extract these features manually,objectify the feature values,and determine their weights so as to provide follow-up guidance to take-out businesses.Through in-depth discussion on the attractiveness of food images and quantitative analysis using machine learning and other technologies,this study can better understand the deficiencies of food picture information,and provide references and possibilities for the research of traditional library and information institutions and the processing of takeaway food image information.At the same time,it provides a basis and reference for further research in related fields,and also provides a scientific basis for businesses to develop more effective marketing strategies.