写一段实现Django房源协同过滤推荐的代码

好的，我可以为您写一段实现Django房源协同过滤推荐的代码：

# 引入必要的库和模块
from django.shortcuts import render
from django.http import HttpResponse
from .models import House, User, Rating
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity

# 实现协同过滤推荐
def collab_filtering(user_id):
    # 获取用户评分数据
    users_ratings = Rating.objects.filter(user=user_id)
    # 获取所有用户的评分数据
    all_ratings = Rating.objects.all()
    # 创建一个矩阵来存储所有用户的评分数据
    user_rating_matrix = np.zeros((len(User.objects.all()), len(House.objects.all())))
    # 将用户评分数据加入到矩阵中
    for rating in users_ratings:
        user_rating_matrix[rating.user.id - 1][rating.house.id - 1] = rating.rating
    # 计算余弦相似性矩阵
    similarity_matrix = cosine_similarity(user_rating_matrix)
    # 获取与当前用户最相似的用户
    sim_users = np.argsort(similarity_matrix[user_id-1])[::-1][:5]
    # 从相似用户中获取已评价过的房源列表
    sim_users_ratings = Rating.objects.filter(user__in=sim_users).exclude(user=user_id)
    # 统计每个房源的平均评分
    avg_ratings = {}
    for house in House.objects.all():
        avg_ratings[house.id] = np.mean(Rating.objects.filter(house=house).values_list('rating', flat=True))
    # 计算每个房源的预测评分
    predictions = {}
    for house in House.objects.all():
        if not Rating.objects.filter(user=user_id, house=house).exists():
            numerator = 0
            denominator = 0
            for rating in sim_users_ratings.filter(house=house):
                similarity = similarity_matrix[user_id-1][rating.user.id-1]
                numerator += similarity * (rating.rating - avg_ratings[rating.house_id])
                denominator += np.abs(similarity)
            if denominator != 0:
                predictions[house] = avg_ratings[house.id] + (numerator / denominator)
            else:
                predictions[house] = avg_ratings[house.id]
    # 获取预测评分最高的10个房源
    recommended_houses = sorted(predictions.items(), key=lambda x: x[1], reverse=True)[:10]
    return recommended_houses

# 创建一个视图函数用于展示推荐结果
def recommend(request):
    # 获取请求中的当前用户ID
    user_id = request.GET.get('user_id')
    # 调用协同过滤推荐函数获取推荐结果
    recommended_houses = collab_filtering(int(user_id))
    # 将结果渲染到页面中
    context = {'user_id': user_id, 'recommended_houses': recommended_houses}
    return render(request, 'recommend.html', context)

以上是一个简单的Django房源协同过滤推荐的代码实现，仅供参考。