房屋租赁管理信息系统的基于用户的协同过滤算法关键代码

房屋租赁管理信息系统的基于用户的协同过滤算法可以分为以下几个步骤：

1. 获取用户历史数据
获取用户历史数据，包括用户对某些房屋的租赁记录，评分等信息。

2. 计算相似度
计算用户之间的相似度，可以使用余弦相似度或者皮尔逊相关系数等方法。

3. 寻找邻居
根据相似度，寻找与当前用户最相似的K个邻居，K值通常取5-20之间。

4. 生成推荐列表
对于每一个邻居，获取其租赁记录，并计算其与当前用户未租赁房屋的预测评分，将预测评分按照从大到小排序，生成推荐列表。

下面是基于用户的协同过滤算法的关键代码实现：

# 获取用户历史数据
user_history = {
    "user1": {"house1": 4, "house2": 5, "house3": 3},
    "user2": {"house2": 3, "house4": 4, "house5": 5},
    "user3": {"house1": 5, "house3": 4, "house6": 3},
    ...
}

# 计算相似度
def cosine_similarity(user1, user2):
    numerator = sum([user_history[user1][item] * user_history[user2][item] for item in user_history[user1] if item in user_history[user2]])
    denominator = math.sqrt(sum([user_history[user1][item]**2 for item in user_history[user1]])) * math.sqrt(sum([user_history[user2][item]**2 for item in user_history[user2]]))
    return numerator / denominator

# 寻找邻居
def find_neighbors(user, k):
    similarities = [(other_user, cosine_similarity(user, other_user)) for other_user in user_history if other_user != user]
    neighbors = sorted(similarities, key=lambda x: x, reverse=True)[:k]
    return neighbors

# 生成推荐列表
def generate_recommendations(user, k):
    neighbors = find_neighbors(user, k)
    recommendations = []
    for neighbor in neighbors:
        neighbor_history = user_history[neighbor]
        for item in neighbor_history:
            if item not in user_history[user]:
                score = sum([neighbor_history[item] * similarity for (other_user, similarity) in neighbors if item in user_history[other_user]])
                recommendations.append((item, score))
    recommendations = sorted(recommendations, key=lambda x: x, reverse=True)
    return recommendations