Python Django网页界面协同过滤推荐算法实现商品管理与推荐

import numpy as np
# 创建一个用户-电影评分矩阵
ratings = np.array([
    [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):
    """计算两个向量之间的余弦相似度"""
    return np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))
def recommend_movies(ratings, user_index):
    """为指定用户推荐电影"""
    scores = []
    target = ratings[user_index]
    for i, user_ratings in enumerate(ratings):
        if i != user_index:
            score = cosine_similarity(target, user_ratings)
            scores.append((i, score))
    scores.sort(key=lambda x: x[1], reverse=True)
    print("最相似的用户索引和相似度分数:", scores)
    # 取出最相似用户的评分
    similar_user_ratings = ratings[scores[0][0]]
    # 找出该用户未评分但相似用户评分高的电影
    recommendations = []
    for i in range(len(similar_user_ratings)):
        if target[i] == 0 and similar_user_ratings[i] >= 4:
            recommendations.append(i)
    return recommendations
# 推荐电影给用户0
print("推荐给用户0的电影索引:", recommend_movies(ratings, 0))