自营音乐

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

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

# 创建用户-音乐评分矩阵
user_music_matrix = df.pivot(index='user_id', columns='music_id', values='rating').fillna(0)

# 计算用户相似度
user_similarity = cosine_similarity(user_music_matrix)

# 推荐音乐
def recommend_music(user_id, user_similarity, user_music_matrix, top_n=3):
    similar_users = user_similarity[user_id - 1].argsort()[::-1][1:]
    recommended_music = set()
    for similar_user in similar_users:
        music_ids = user_music_matrix.iloc[similar_user].dropna().index
        recommended_music.update(music_ids)
        if len(recommended_music) >= top_n:
            break
    return list(recommended_music)[:top_n]

# 示例推荐
recommended_music = recommend_music(user_id=1, user_similarity=user_similarity, user_music_matrix=user_music_matrix)
print("Recommended music for user 1:", recommended_music)