基于协同过滤的推荐算法和基于热度的推荐算法结合生成算法代码

好的，下面是一个基于协同过滤和热度的推荐算法结合的代码实现：

import numpy as np

class CollaborativeFiltering:
    def __init__(self, num_users, num_items):
        self.num_users = num_users
        self.num_items = num_items
        self.ratings = np.zeros((num_users, num_items))
        
    def fit(self, X):
        self.ratings = X.copy()
        
    def predict(self, user_id):
        user_ratings = self.ratings[user_id]
        non_zero_indices = np.nonzero(user_ratings)[0]
        if len(non_zero_indices) == 0:
            return np.zeros(self.num_items)
        else:
            similarities = np.zeros(self.num_users)
            for i in non_zero_indices:
                similarities += self.ratings[:,i] * user_ratings[i]
            similarities /= np.sum(user_ratings)
            similarities[user_id] = 0
            top_k_users = np.argsort(-similarities)[:10]  # 取前10个相似度最高的用户
            predictions = np.zeros(self.num_items)
            for i in range(self.num_items):
                numerator = 0
                denominator = 0
                for j in top_k_users:
                    if self.ratings[j,i] != 0:
                        numerator += similarities[j] * self.ratings[j,i]
                        denominator += similarities[j]
                if denominator != 0:
                    predictions[i] = numerator / denominator
            return predictions
        
class PopularityBased:
    def __init__(self, num_items):
        self.num_items = num_items
        self.popularity = np.zeros(num_items)
        
    def fit(self, X):
        self.popularity = np.sum(X, axis=0)
        
    def predict(self):
        return self.popularity / np.sum(self.popularity)
    
class HybridRecommendation:
    def __init__(self, num_users, num_items):
        self.collaborative_filtering = CollaborativeFiltering(num_users, num_items)
        self.popularity_based = PopularityBased(num_items)
        
    def fit(self, X):
        self.collaborative_filtering.fit(X)
        self.popularity_based.fit(X)
        
    def predict(self, user_id):
        collaborative_filtering_predictions = self.collaborative_filtering.predict(user_id)
        popularity_based_predictions = self.popularity_based.predict()
        return (collaborative_filtering_predictions + popularity_based_predictions) / 2

这个代码实现了一个基于协同过滤和热度的推荐算法结合的混合推荐算法。其中，`CollaborativeFiltering` 类实现了基于用户的协同过滤算法，`PopularityBased` 类实现了基于热度的推荐算法，`HybridRecommendation` 类则是将两种算法结合起来，使用加权平均的方式生成最终的推荐结果。在 `predict` 方法中，首先调用 `CollaborativeFiltering` 类的 `predict` 方法得到基于协同过滤的推荐结果，然后调用 `PopularityBased` 类的 `predict` 方法得到基于热度的推荐结果，并将两个结果取加权平均得到最终的推荐结果。