Restaurant ordering recommendation system based on data mining

Collect and follow to avoid getting lost

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Technology Introduction

  

2.1 JAVA language

Java is an Internet-oriented development tool that is easy to use, completely object-oriented, platform-independent, safe and reliable. Since its official introduction in 1995, the rapid development of Java has brought about earth-shaking changes in the entire online world. Java has become the preferred development tool for Web-based applications, and current Java technology has become the inevitable choice for all large-scale e-commerce projects. [3]

2.2 Data mining technology

Data mining refers to the process of finding information hidden in large amounts of data through algorithms. Data mining is a technology that analyzes each data to find patterns in a large amount of data. It mainly consists of three steps: data preparation, pattern search and pattern representation. Data preparation is to select the required data from relevant data sources and integrate it into a data set for data mining; finding patterns is to use a certain method to find out the patterns contained in the data set; pattern representation is to be as user-friendly as possible. The way of understanding is to express the found rules. In recent years, data mining has attracted great attention from the information industry. The main reason is that there is a large amount of data and there is an urgent need to convert these data into useful information and knowledge. And the information and knowledge gained can be used in a wide range of applications, including business management, market analysis, engineering design, and scientific exploration.

2.3 Collaborative filtering algorithm

The collaborative filtering algorithm is a well-known and commonly used recommendation algorithm. It discovers user preferences based on mining historical user behavior data, and predicts products that users may like to recommend, which are the common "guess you like" and "purchase" People also like this product" and other functions. Its main implementation consists of recommending you based on people who share common interests with you, recommending similar items to you based on the items you like, and comprehensive recommendations based on the above conditions. Therefore, it can be concluded that commonly used collaborative filtering algorithms are divided into two types, namely user-based collaborative filtering algorithm and item-based collaborative filtering algorithm. The characteristics can be summarized as "people of the same kind flock together, and things gather in groups", and predictions and recommendations can be made based on this.

1. System requirements analysis

3.1 Analysis of system functional requirements

This restaurant ordering recommendation system provides two major functions: front-end dish display and back-end management. Customers log in to the main page of the restaurant's ordering recommendation system to browse and query the dishes. Then the customer confirms the dishes they need to buy online by registering a user name and logging in, and adds these dishes to the shopping cart, and finally fills in the Orders, checkout and order inquiries. Backend management is mainly for administrators to manage dishes and user information by logging in, including functions such as viewing, adding, modifying, deleting dish information, viewing user information, etc. In addition, a visual statistics module has been added to realize the visual display of dish sales information.

3.1.1 Dishes display module

The dish display module mainly enables users to query the dishes they are interested in and learn about the dishes stored in the restaurant when they browse to the main page of the restaurant ordering recommendation system. When the user logs in, he can not only browse and query the dishes, but also display the shopping cart and collection buttons under the corresponding dishes, making it convenient for the user to put the dishes on his shopping cart and collect them. In addition, the system can also recommend dishes based on the user's operations, which greatly saves the user's time, thereby giving customers a high-quality user experience and making users willing to continue to choose this ordering system.

3.1.2 Shopping cart module

The shopping cart module is mainly responsible for storing the dishes that users need to purchase. It mainly enables users to add the dishes they need to their shopping carts when they see them; view the shopping cart to enable users to understand the items in their shopping carts. ;Remove items from the shopping cart is responsible for removing items that you do not want to buy from the shopping cart; the clear shopping cart function allows the shopping cart to be emptied at once. It should be noted that during the design, in addition to the modification and clearing functions of the number of dishes in the shopping cart, hyperlinks to the homepage and checkout were also established to facilitate users to continue ordering.

3.1.3 Cashier module

Although the design of the cashier module is simple, it is a more important module in the design of this restaurant ordering recommendation system. Without the cashier module, users cannot implement the payment function. The main functions of the module include filling in order information and displaying order results.

3.1.4 Personal management module

The personal management module provides modification of personal information, order inquiry, viewing of favorite dishes, modification of passwords and personal delivery address. The personal information includes real name, gender, email, phone number and address.

3.1.5 Order query module

The main function of the order query module is to facilitate users to query all their orders, but users cannot perform specific operations on these orders, ensuring the uniqueness and certainty of the orders.

3.1.6 Dishes management module

The dish management module is the core module of the backend of this restaurant's ordering recommendation system. Its main functions include: viewing dish information, adding dish information, modifying dish information, and deleting dish information. When viewing dish information, all dishes will be displayed in a table, with a simple and clear interface. Modify the dish information. Since the dish number is a unique number, this item cannot be changed. Add and delete dish information, and the administrator can arrange the listing and removal of dishes according to the restaurant.

3.1.7 User management module

The user management module is a platform for administrators to manage users. The main function is to view user information. The design of this module is relatively easy.

3.1.8 Order management module

Order management mainly allows administrators to view order information and execute orders based on the user's remittance payment status.

3.1.9 Announcement and message management module

Announcement management provides the functions of viewing announcement information, adding announcement information, and deleting announcement information. Announcements added by the administrator will be displayed on the announcement board at the front desk. Message management provides the function of viewing and deleting message information.

3.1.10 Statistics management module

The statistics management module provides the administrator with the function to view information related to restaurant dish sales. These functions make it easier for restaurant managers to understand users' needs and help arrange meals reasonably.

2. Outline design

4.1 System function diagram

Based on the early analysis and customer needs, the front desk of this restaurant ordering recommendation system mainly includes guessing what you like, dish recommendations, dish category browsing, adding to shopping cart, viewing shopping cart, removing dishes from shopping cart, and clearing shopping cart. , filling in order information, checkout, user registration, user login, personal information modification, and order query modules. The front desk functional structure of this restaurant ordering recommendation system is shown in Figure 4-1.

Figure 4-1 Front-end functional structure diagram
　　The back-end module of the restaurant ordering recommendation system mainly includes viewing dish information, adding dish information, modifying dish information, deleting dish information, and viewing users Information, view order information, execute orders, and exit backend management. The backend functional structure of the restaurant ordering recommendation system is shown in Figure 4-2.
　　

Figure 4-2 Backend function structure diagram

3. System display

4. Conclusion

  　After this graduation project and the collection of relevant materials, I clearly feel that with the continuous development of network technology and the widespread application of the network, our lives are increasingly inseparable from it. We are conquered by our own unique advantages. In the current fast-paced 21st century, with the continuous increase of business types and the continuous improvement of business management requirements, the workload of restaurant management will become larger and larger, and its work will be very cumbersome and very error-prone. things. Under such circumstances, the emergence of a practical restaurant ordering recommendation system is inevitable. If a complete online ordering system can be made, the management workload will be reduced a lot. This system can reduce the work burden of catering workers to a great extent, greatly save diners' time, bring high-quality customer experience to diners, and bring economic benefits to the restaurant to a large extent. Most importantly, Yes, during the current epidemic, it plays a role in diversion of users for restaurants and greatly reduces the number of people gathering. In this graduation project, I learned a lot, and I also feel that my knowledge is poor. I hope to make a more complete system in my future efforts.
　　During the development process of the system, I adopted B/S structure technology and some technologies that I use in daily study. The implementation of these technologies has greatly improved the performance of the entire system. These The techniques are all introduced in detail in the paper. There are still many flaws and shortcomings in this system. For example, many details are not good enough. Some functional modules need to be strengthened. Other factors such as season and healthy combination of dishes are not considered when making recommendations. I hope that these shortcomings can be made up for in the future. Further improve the system.