12.12智能识别推荐

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)

# 创建用户-商品评分矩阵
user_item_matrix = df.pivot(index='user_id', columns='item_id', values='rating').fillna(0)

# 计算用户相似度
user_similarity = cosine_similarity(user_item_matrix)
user_similarity_df = pd.DataFrame(user_similarity, index=user_item_matrix.index, columns=user_item_matrix.index)

def get_similar_users(user_id):
    return user_similarity_df[user_id].sort_values(ascending=False).index.tolist()[1:]  # 排除自身

def recommend_items(user_id):
    similar_users = get_similar_users(user_id)
    recommended_items = set()
    for similar_user in similar_users:
        items_rated_by_similar_user = user_item_matrix.loc[similar_user][user_item_matrix.loc[similar_user] > 0].index.tolist()
        recommended_items.update(items_rated_by_similar_user)
    return list(recommended_items)

# 示例：为用户1推荐商品
recommended_items_for_user_1 = recommend_items(1)
print(f"为用户1推荐的商品ID: {recommended_items_for_user_1}")