Przegląd Piekarski i Cukierniczy październik 2024 Recommendation systems are a key element in modern online stores, helping users find products that might interest them and increasing sales. These systems are built based on sales records, as online sales are not anonymous. Each customer must log into the store before making a purchase, so all information on customer choices and transactions is meticulously stored. This allows us to predict what a customer may purchase in the future and identify similar customer groups with similar preferences, enabling the creation of applications that significantly improve an online store’s sales statistics. Building such a system requires combining data analysis algorithms with a solid technological foundation. Django, a popular Python-based framework, is one of the tools enabling the development of efficient and scalable recommendation systems. In this article, I’ll discuss why Django is a good choice for such projects, how it works, and provide a simple example of a recommendation system that can be created and deployed using it. Django is a web framework, meaning it is used directly on the website of the store. It simplifies the creation and maintenance of web applications. Django has several advantages for recommendation systems, including rapid prototyping, which enables quick creation of prototypes using built-in tools for managing databases, forms, views, and sessions. Prototyping is the process of creating an initial, simplified version of a product, system, or application to test basic functions, ideas, or concepts before full deployment and development. Prototypes are generally simpler than the final version but allow for quick testing of how a solution will work in practice, identifying errors, or collecting user feedback. In recommendation systems, one can prototype quickly, either in the commonly mentioned Jupyter Notebook or in Django, as discussed here. Another valuable feature of Django is scalability. With Django, you can create applications that grow along with the development of the online store. Its ability to integrate with various databases, caching systems, and servers enables applications to handle an increasing number of users, which is crucial for a dynamically expanding business. Data security is another important aspect. Django ensures application security by providing automatic protection against common threats, such as SQL Injection or Cross-site Scripting (XSS). In an online store, a data breach can be a significant issue. Django offers ready-made mechanisms for managing user login and permissions. Django also facilitates communication with the database through Python objects and automatically creates an admin panel based on database models, making content and user management quicker. Imagine a company selling sports items through an online store. The company offers various product categories, such as footwear, clothing, sports equipment, and exercise accessories. The online store already has a user base that regularly makes purchases. The store owners aim to implement a recommendation system to increase the cart value by suggesting products frequently bought together. In a simple recommendation system, it’s noticeable that some products are frequently bought together. For example, if a customer buys running shoes, there’s a high chance they might also be interested in running socks. A recommendation system could work by analyzing historical purchase data. An algorithm could monitor customer purchases to discover which products are often bought together. Such an analysis could lead to recommendations where, if a user adds shoes to their cart, the system suggests adding socks as well. This solution is known as pair-recommendation. In Python, using Jupyter Notebook, one can create a simple recommendation system. For instance, the Pandas library can be used for purchase data analysis. While Jupyter Notebook is an excellent tool for prototyping and data analysis, it has several limitations in a real online store context. Jupyter is an offline data analysis tool and lacks integration with real-time user traffic. What we created is merely an experiment or a kind of prototype. Another barrier to using our solution in practice is performance. Jupyter is not optimized for high performance or for handling a large number of users simultaneously. Additionally, it lacks web functionalities, such as authorization, user sessions, and other tools needed for building an online store. Django, unlike Jupyter Notebook, allows you to create a scalable web application that can operate in real-time. The implementation process for a recommendation system in Django could look as follows: Data Model: Django allows defining models for products, orders, and users, which can be easily stored in a database using ORM (Object-Relational Mapping). User Interface: Django enables displaying recommendations directly on the store’s website by integrating views with HTML templates. When a user views their cart, they could see recommended products on the same page. With a little determination, an online store owner could learn the basics of Django and implement a recommendation system. This could be worthwhile, as recommendation systems tend to boost sales, regardless of their effectiveness. The presented example is a simple recommendation system, but there are many possibilities for creating more complex systems. Building a recommendation system in Python in a Jupyter Notebook environment is relatively straightforward. The challenge is deploying it to the cloud and connecting it to existing websites, a process that can be costly and time-consuming. It’s worth learning Django to be self-sufficient and agile in the e-commerce market.
MOSZCZYNSKI 10-24
Application of Django in Recommendation Systems: From Prototype to Scalable Solution
Why Use Django?
Example of a Company Selling Products Online
Simple Recommendation System
Implementing in Jupyter Notebook
Why Jupyter is Insufficient in Practice
Deploying the Recommendation System with Django
Summary