智能机器人推荐

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import linear_kernel

# 假设我们有以下电影数据和用户偏好
movies = ['The Dark Knight', 'Inception', 'Interstellar', 'The Matrix']
descriptions = [
    'Batman fights Joker in this epic action thriller.',
    'A thief who steals corporate secrets through the use of dream-sharing technology...',
    'A team of explorers travel through a wormhole in space in an attempt to ensure humanity\'s survival.',
    'A computer hacker learns from mysterious rebels about the true nature of his reality and his role in the war against its controllers.'
]
user_preference = 'space adventure'

# 使用TF-IDF向量化描述
tfidf = TfidfVectorizer(stop_words='english')
tfidf_matrix = tfidf.fit_transform(descriptions)

# 计算相似度
cosine_sim = linear_kernel(tfidf_matrix, tfidf_matrix)

# 推荐函数
def get_recommendations(title, cosine_sim=cosine_sim):
    idx = movies.index(title)
    sim_scores = list(enumerate(cosine_sim[idx]))
    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)
    sim_scores = sim_scores[1:3]  # 获取最相似的两个
    movie_indices = [i[0] for i in sim_scores]
    return movies[movie_indices]

# 为用户偏好推荐电影
recommended_movies = get_recommendations(user_preference)
print(recommended_movies)