双十一视频智能分析购买

import pandas as pd
from sklearn.metrics.pairwise import cosine_similarity

# 假设我们有一个用户-商品评分矩阵
data = {
    'user_id': [1, 1, 2, 2, 3, 3],
    'item_id': [101, 102, 101, 103, 102, 104],
    'rating': [5, 3, 4, 1, 5, 2]
}
df = pd.DataFrame(data)

# 计算商品之间的相似度
item_similarity = cosine_similarity(df.pivot(index='user_id', columns='item_id', values='rating').fillna(0))

def recommend_items(user_id, item_similarity=item_similarity):
    user_ratings = df[df['user_id'] == user_id].set_index('item_id')['rating']
    similar_items = item_similarity.dot(user_ratings) / item_similarity.sum(axis=1)
    recommended_items = similar_items.sort_values(ascending=False).index.tolist()
    return recommended_items

# 为用户1推荐商品
print(recommend_items(1))