Design And Implementation Of Self-service System And Its Recommendation Engine

With the development of the Internet of Things technology,commodity sales in the context of the Internet of Things are becoming more and more popular,and the demand for commodity recommendations is becoming stronger.However,the traditional IOT sales model has problems such as inconvenient product purchase process,high management and operation costs,and difficulty in product recommendation.Based on the above-mentioned problems,this paper designs and implements a self-service system and its recommendation engine.The self-service system realizes the function of IOT vending and unified management of commodities and vending machine equipment.The recommendation engine implements product recommendation in the context of the Internet of Things,so that users can accurately obtain the products they want.The self-service system adopts the design mode of separating the front and back ends.It consists of two parts: the client and the Io T cloud platform.The client uses the We Chat developer platform for development,and builds the software architecture based on the MVC framework.Realize functions such as scanning code recognition,i Beacon search,user authentication,product presentation and Io T cloud platform docking.The Io T cloud platform is developed based on Nginx server,Springboot framework and Netty framework.Realize security gateway and load balancing based on Nginx server.Build a back-end server based on the Springboot framework to implement business service functions such as user authentication,transaction payment,and product information management.Build a long connection server based on the Netty framework to realize device service functions such as device docking,device information management,device control,and device message return.The recommendation engine includes four functional modules: human-computer interaction,structured representation,reasoning and decision-making,and data storage.The recommendation algorithm is designed based on Answer Set Programming(ASP).And take the sales of lipstick products as an example to discuss the implementation of the recommendation algorithm in detail.The recommendation engine provides a human-computer interaction interface to help users enter information,and uses the framework knowledge representation method to standardize the information input to the recommendation engine and extract structured information.According to the grammatical specification of answer set programming,the extracted information is represented as The answer set sentence is added to the reasoning decision module for reasoning and solving.When the recommendation engine completes the product recommendation function,it will find the most suitable product for the user's mouth in the fact database,and then go to the database to obtain the detailed information of the product and present it to the user.At the end of this article,the self-service system is deployed online,and the relevant functional interfaces of the self-service system and the recommendation engine are displayed.The results show that the self-service system proposed in this paper improves the user's product purchase experience and effectively manages the operation and maintenance of the vending machine equipment and its products.The proposed recommendation engine effectively improves the effect of product recommendation in the context of the Internet of Things,and solves the difficulty of recommendation caused by a single product category and a small sample size of the product in the Internet of Things scenario.