Python Django Web Interface Collaborative Filtering Recommendation Algorithm Implementation for Product Management and Recommendation

present (sb for a job etc)

Innovation: Using collaborative filtering algorithms to achieve personalized recommendations for currently logged-in users in the guess-your-favorite interface based on users' ratings of products.

The main features are:

• The system is divided into two roles: user and administrator.
• Users can login, register, view products, purchase products, add cart, post comments, rate products, view cart, edit personal information, recharge, etc.

Administrators can manage users and products in the back-end management system

System function effect picture display

Introduction to Collaborative Filtering Algorithms

Collaborative filtering algorithms are a technique widely used in recommender systems based on the simple assumption that if two people liked the same thing in the past, they are likely to like similar things in the future. Such algorithms are usually divided into two categories: user-based collaborative filtering and item-based collaborative filtering.

• User-based collaborative filtering: this approach first identifies other users with similar interests to the target user and then recommends items to the target user based on the preferences of these similar users.
• Item-based collaborative filtering: in contrast, this approach first identifies other items that are similar to the target item and then recommends those items to those users who like the target item.

Now, let's implement a simple user-based collaborative filtering algorithm in Python. We will create a small dataset of movie ratings and recommend movies based on the similarity of users' ratings.

import numpy as np
# Create a user-movie rating matrix
ratings = ([
    [5, 4, 1, 1, 3],
    [3, 2, 1, 3, 3],
    [4, 3, 3, 1, 5],
    [3, 3, 1, 2, 4],
    [1, 5, 5, 2, 1],
])
def cosine_similarity(v1, v2):
    """Compute the cosine similarity between two vectors."""
    return (v1, v2) / ((v1) * (v2))
def recommend_movies(ratings, user_index):
    """Suggest a movie for a given user."""
    scores = []
    target = ratings[user_index]
    for i, user_ratings in enumerate(ratings):
        if i != user_index:
            score = cosine_similarity(target, user_ratings)
            ((i, score))
    (key=lambda x: x[1], reverse=True)
    print("Index of most similar users and similarity scores:", scores)
    # Take out the ratings of the most similar users
    similar_user_ratings = ratings[scores[0][0]]
    # Find movies not rated by this user but rated highly by similar users
    recommendations = []
    for i in range(len(similar_user_ratings)):
        if target[i] == 0 and similar_user_ratings[i] >= 4:
            (i)
    return recommendations
# Recommended movies to users 0
print("Index of movies recommended to user 0:", recommend_movies(ratings, 0))

In this code, we first define a simple user-movie rating matrix and then use cosine similarity to compute the similarity between different users. Based on these similarity scores, we find out the users who are most similar to the target user, and then recommend movies that are not rated by the target user but are highly rated by similar users. This is a basic collaborative filtering recommendation example.

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