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矩阵分解（Matrix Factorization）是一种常用的协同过滤（Collaborative Filtering, CF）算法，常用于推荐系统中。下面是一个基于矩阵分解的CF算法的实现示例：
import numpy as np class MatrixFactorizationCF: def __init__(self, num_users, num_items, num_factors=10, learning_rate=0.01, reg_param=0.01, num_iterations=100): self.num_users = num_users self.num_items = num_items self.num_factors = num_factors self.learning_rate = learning_rate self.reg_param = reg_param self.num_iterations = num_iterations self.user_factors = None self.item_factors = None def fit(self, train_data): # 初始化用户和物品的隐因子矩阵 self.user_factors = np.random.normal(scale=1./self.num_factors, size=(self.num_users, self.num_factors)) self.item_factors = np.random.normal(scale=1./self.num_factors, size=(self.num_items, self.num_factors)) for iteration in range(self.num_iterations): for user_id, item_id, rating in train_data: error = rating - self.predict(user_id, item_id) # 更新用户和物品的隐因子矩阵 self.user_factors[user_id] += self.learning_rate * (error * self.item_factors[item_id] - self.reg_param * self.user_factors[user_id]) self.item_factors[item_id] += self.learning_rate * (error * self.user_factors[user_id] - self.reg_param * self.item_factors[item_id]) def predict(self, user_id, item_id): return np.dot(self.user_factors[user_id], self.item_factors[item_id]) 使用示例：
# 创建一个矩阵分解的CF模型 cf_model = MatrixFactorizationCF(num_users=100, num_items=50, num_factors=10, learning_rate=0.01, reg_param=0.01, num_iterations=100) # 使用训练数据训练模型 train_data = [(0, 0, 5), (1, 1, 3), (2, 2, 4), ...] cf_model.fit(train_data) # 预测用户0对物品1的评分 user_id = 0 item_id = 1 predicted_rating = cf_model.predict(user_id, item_id) print("Predicted rating for user", user_id, "and item", item_id, ":", predicted_rating) 以上示例演示了如何使用基于矩阵分解的CF算法对用户对物品的评分进行预测。在fit方法中，通过迭代优化用户和物品的隐因子矩阵，来逼近真实的评分数据。然后使用predict方法来预测用户对物品的评分。
