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Based on Python and Django for Tourist Attraction Recommendation System

Author Bio: Years of experience in Java, front-end, and Python development, including work as a senior developer, project manager, and architect.

Main Content: Java project development, Python project development, university data and AI projects, microcontroller design projects, technical interview preparation, sharing of latest technologies.

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 Project Number: BS-Python-063

I. Environment Introduction

Language environment: Python 3.8

Database: MySQL: mysql5.7

WEB framework: Django

Spider framework: Scrapy

Development tools: IDEA or PyCharm

Project acquisition address: Travel Scenic Spot Recommendation System

Direct access address:

II. Project Overview

Based on the study of travel scenic spot recommendation content, a travel scenic spot recommendation system is designed and implemented. The main functions that the system can achieve should include operations on scenic spot information and park tickets. It should also be able to correctly serve users and accurately display current information.

In the early stage of the travel scenic spot recommendation system, i.e., during the requirements analysis phase, we provided a detailed description of the requirements for travel scenic spot recommendations. These are elaborately described and explained in the requirements specification. Based on the analysis of system requirements, an overall design of the management of travel scenic spot recommendations was carried out. A detailed analysis of software module design was emphasized to meet system requirements. This section focuses on explaining system division, interface determination, data transmission between modules, as well as data structure and module structure design. In the detailed design below, this stage of the system will be described in detail.

Admin Use Case:

User Use Case:

System Function Architecture Diagram:

III. System Demonstration

Front-end Page Implementation

Back-end Admin Function Demonstration




Index Page

Welcome to Index Page

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5. Related Works Showcase

Practical projects developed using Java, Python, PHP, C#, and other programming languages.

Frontend practical projects developed using Node.js, Vue.js, and other frontend technologies.

Works related to WeChat mini-programs and Android APP application development.

Applications based on 51 microcontroller and other embedded IoT devices.

AI intelligent applications implemented through various algorithms.

Data management and recommendation systems realized using big data technology.

 

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10 responses to “Based on Python and Django for Tourist Attraction Recommendation System”

  1. Overall, this is an excellent resource for developers aiming to build a travel recommendation system. The clear structure, detailed explanations, and practical code make it highly recommended.

  2. For someone looking to dive into AI and big data projects, this article offers valuable insights and practical examples. The detailed demonstrations of front-end and back-end functionalities are a great learning resource.

  3. The article successfully bridges the gap between theory and practice by providing a real-world implementation of a tourist attraction recommendation system using Python and Django.

  4. I found the admin use case section particularly useful. It demonstrates how to manage scenic spot information and park tickets efficiently, which is crucial for any recommendation system.

  5. The author’s expertise is evident in the way they explain complex concepts like system architecture and module design in an accessible manner. The code snippets are also very helpful for reference.

  6. This article is a must-read for anyone interested in building a travel recommendation system. The detailed requirements analysis and design phases provide a solid foundation for understanding the project structure.

  7. The inclusion of practical projects and related works adds value to this article. It’s a great resource for learning AI-based recommendation systems and web development with Python and Django.

  8. I appreciate how the article breaks down the system into admin and user functionalities. The front-end and back-end demonstrations are well-explained, making it easy to follow for both beginners and experienced developers.

  9. The use of Django and Scrapy frameworks, along with MySQL database, ensures a robust backend for the recommendation system. The author’s experience shines through in the clear explanations and code examples provided.

  10. This article provides a comprehensive guide to building a tourist attraction recommendation system using Python and Django. The detailed project overview and step-by-step implementation make it an excellent resource for developers.

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