教育行业人工智能

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

# 示例数据
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-物品评分矩阵
user_item_matrix = df.pivot_table(index='user_id', columns='item_id', values='rating')

# 计算相似度矩阵
similarity_matrix = cosine_similarity(user_item_matrix.fillna(0))

# 推荐函数
def recommend_items(user_id, num_recommendations=2):
    user_index = user_id - 1
    similarity_scores = list(enumerate(similarity_matrix[user_index]))
    similarity_scores = sorted(similarity_scores, key=lambda x: x[1], reverse=True)
    similar_users = [i[0] for i in similarity_scores[1:num_recommendations+1]]
    
    recommended_items = set()
    for user in similar_users:
        items = user_item_matrix.iloc[user].dropna().index
        recommended_items.update(items)
    
    return list(recommended_items - set(user_item_matrix.iloc[user_index].dropna().index))

# 推荐示例
print(recommend_items(1))