PHP Framework
Workerman
Building a Shopping Website with a Powerful Recommendation Engine: Webman's Guide to Shopping Applications
Building a Shopping Website with a Powerful Recommendation Engine: Webman's Guide to Shopping Applications
Building a shopping website with a powerful recommendation engine: Webman’s Shopping Application Guide
With the rapid development of the Internet, the way of online shopping has become a part of modern people’s lives. An important part of. In order to allow users to have a better shopping experience, a shopping website with a powerful recommendation engine is essential. In this article, we'll cover how to build a shopping app called Webman that features a great recommendation engine.
First of all, we need to build the basic framework of the website. We can use Python's Django framework to quickly build a stable shopping website. The following is a simple sample code used to build the basic framework of a shopping website:
from django.urls import path
from . import views
urlpatterns = [
path('', views.home, name='home'),
path('products/', views.product_list, name='product_list'),
path('product/<int:product_id>/', views.product_detail, name='product_detail'),
]In the above code, we define three paths: homepage, product list, and product details. Next, we need to define the corresponding view functions to handle these paths.
from django.shortcuts import render
from .models import Product
def home(request):
return render(request, 'home.html')
def product_list(request):
products = Product.objects.all()
return render(request, 'product_list.html', {'products': products})
def product_detail(request, product_id):
product = Product.objects.get(pk=product_id)
return render(request, 'product_detail.html', {'product': product})In the above code, we associate the template file with the view function through Django's render function. Next, we need to define the corresponding template file to render the page.
The code for the homepage template (home.html) is as follows:
<!DOCTYPE html>
<html>
<head>
<title>Webman购物应用</title>
</head>
<body>
<h1 id="欢迎来到Webman购物应用">欢迎来到Webman购物应用</h1>
</body>
</html>The code for the product list template (product_list.html) is as follows:
<!DOCTYPE html>
<html>
<head>
<title>Webman购物应用</title>
</head>
<body>
<h1 id="产品列表">产品列表</h1>
<ul>
{% for product in products %}
<li><a href="/product/{{ product.id }}/">{{ product.name }}</a></li>
{% endfor %}
</ul>
</body>
</html>Product details template The code of (product_detail.html) is as follows:
<!DOCTYPE html>
<html>
<head>
<title>Webman购物应用</title>
</head>
<body>
<h1 id="product-name">{{ product.name }}</h1>
<p>{{ product.description }}</p>
<p>价格：{{ product.price }}</p>
</body>
</html>Now, we can build a basic shopping website. Next, let's start implementing a powerful recommendation engine.
The core of the recommendation engine is to recommend related products to users based on their preferences and behaviors. Below is a simple sample code for building a recommendation engine based on user preferences.
from .models import Product, UserBehavior
def recommend_products(user_id):
user_behavior = UserBehavior.objects.filter(user_id=user_id)
viewed_products = user_behavior.filter(action='view')
bought_products = user_behavior.filter(action='buy')
similar_users = []
for bought_product in bought_products:
users = UserBehavior.objects.filter(product_id=bought_product.product_id, action='buy').exclude(user_id=user_id)
similar_users.extend(users)
recommended_products = []
for similar_user in similar_users:
products = UserBehavior.objects.filter(user_id=similar_user.user_id, action='view').exclude(product__in=viewed_products)
recommended_products.extend(products)
return recommended_productsIn the above code, we first obtain the user's browsing and purchase records, and then find similar users based on other users' purchase behavior of the same product. Finally, recommendations are made to the current user based on the browsing behavior of similar users.
The above is just a simple sample code, the actual recommendation engine will be more complex. Machine learning algorithms and user behavior models can be used to improve recommendation effects.
With the above code example, we can build a shopping website Webman with a powerful recommendation engine. Users can get personalized product recommendations based on their interests and needs. This will greatly enhance the user's shopping experience and increase the likelihood of purchase.
We hope that the shopping application guidelines described in this article will be helpful to readers who develop shopping websites with powerful recommendation engines. I wish readers can build excellent shopping applications to meet user needs.
The above is the detailed content of Building a Shopping Website with a Powerful Recommendation Engine: Webman's Guide to Shopping Applications. For more information, please follow other related articles on the PHP Chinese website!
Hot AI Tools
Undresser.AI Undress
AI-powered app for creating realistic nude photos
AI Clothes Remover
Online AI tool for removing clothes from photos.
Undress AI Tool
Undress images for free
Clothoff.io
AI clothes remover
AI Hentai Generator
Generate AI Hentai for free.
Hot Article
Hot Tools
Notepad++7.3.1
Easy-to-use and free code editor
SublimeText3 Chinese version
Chinese version, very easy to use
Zend Studio 13.0.1
Powerful PHP integrated development environment
Dreamweaver CS6
Visual web development tools
SublimeText3 Mac version
God-level code editing software (SublimeText3)
Hot Topics
How to bind a workerman user workerman user binding tutorial
Mar 06, 2025 pm 02:37 PM
This article details implementing user authentication and session management within the Workerman framework. It addresses the core issue of Workerman's lack of inherent authentication, outlining methods like username/password, token-based, and OAut
Run multiple workerman instances
Mar 06, 2025 pm 02:38 PM
This article discusses scaling Workerman applications by running multiple instances. It addresses efficient resource management through monitoring, process limits, and load balancing, advocating horizontal scaling. Best practices include stateless
How does workerman distinguish users
Mar 06, 2025 pm 02:31 PM
This article explains how the Workerman framework handles concurrent users and user management. Workerman, an asynchronous event-driven framework, doesn't inherently manage users; application logic using session IDs or token-based authentication han
How to set up a workerman to receive information sound tutorial
Mar 06, 2025 pm 02:32 PM
This article details how to add sound notifications to the Workerman PHP framework. Since Workerman lacks built-in audio capabilities, integration with external libraries (e.g., using system calls or PHP audio libraries) is necessary. Methods incl
How to define the ICTMP protocol tutorial for workerman
Mar 06, 2025 pm 02:36 PM
This tutorial explains why Workerman, a PHP framework, doesn't directly support ICMP. It details how to indirectly use Workerman for ICMP ping operations by leveraging OS-level tools or system calls for packet manipulation, with Workerman managing t
How to reuse asynchronous links workerman reuse asynchronous links tutorial
Mar 06, 2025 pm 02:35 PM
This article addresses efficient asynchronous connection handling in the Workerman PHP framework. It argues that "reusing" connections isn't about explicit pooling, but optimizing Workerman's inherent efficient event loop via proper config
How to call the database workerman database call tutorial
Mar 06, 2025 pm 02:33 PM
This tutorial demonstrates efficient MySQL database interaction within Workerman using PHP and a connection pool. It emphasizes minimizing connection overhead for improved performance under high concurrency, covering best practices like prepared st
How to run bat file steps for workerman
Mar 06, 2025 pm 02:34 PM
This article details using batch files to run a Workerman server. It covers basic startup, background processes, handling potential issues (incorrect paths, dependencies, permissions), and passing arguments to the server for flexible control.
