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Building a Shopping Website with a Powerful Recommendation Engine: Webman's Guide to Shopping Applications

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Aug 25, 2023 am 10:13 AM

Recommendation engineshopping websitewebman shopping app

Building a Shopping Website with a Powerful Recommendation Engine: Webmans 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_products

In 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.

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