The Research And Implementation Of Digital Exhibition And Recommendation System On Printing And Dyeing Samples

In view of the marketing needs of the textile printing and dyeing industry,as well as the variety and complex attributes of printing and dyeing samples,this paper takes the background of a specific project of a textile company,uses the “Internet + smart marketing” technology and analyzes the status and needs of the company and related industries.This paper designs and implements a digital exhibition and recommendation system for the textile printing and dyeing samples to improve the company's marketing quality and efficiency,as well as the overall level of service and reduce marketing costs.This paper analyzes the status of marketing in the industry and company needs firstly,this paper proposes a collaborative filtering algorithm based on customer star rating for a wide variety of printing and dyeing products,the complex attributes and the professional characteristics of sample fabrics.Moreover,to achieve a better recommendation of quality and to protect the customers' resources and customers' privacy.First,this algorithm selects the customer's star rating of sample fabric details and the customer's individual as a parameter,creates a "customer-score" matrix,and calculates the similarity during the customers.The highest number of similar customers as nearest neighbors.Then,similarity is used as a weight,and the weighted average is used to calculate the certain recommendation value of the closest neighbor to a certain sample fabric,and several sample fabrics with the highest score are recommended for the customer.In addition,this paper design relative strategies to solve the problem of sparseness of sample fabric star rating data by customers to improve the efficiency of the algorithm.In the process of recommendation,the system utilized the company's overall customers' statistical information on fabrics to assist specific salespersons in marketing activities.This not only achieved the sharing of knowledge and information,but also protected the privacy of the customers and the customer information of the marketing staff.The results of the recommendation system showed that the samples recommendation system recommended are used up to 80%,the recall rate and accuracy rate are 18.2%,23.4% respectively.Based on the theoretical research of the recommend algorithms,this paper elaborates the design process of the system according to the requirement analysis,and completed the optimization of the coding part function.The system uses the Spring MVC and JQuery Mobile technology to design the digital exhibition system.The system has functions such as showroom management,customer management,data analysis,customer browsing,customer evaluation,and interaction with the backstage supporter.The company's marketing staff can use this system to easily create online digital showrooms for different customers.Customers can also browse the marketer's recommended samples through the browser and mobile to conduct evaluations and complete online communication with marketers.This system is compatible with smart phones and PCs,easy and flexible to operate.Now it has been effectively applied in the actual scene.