python协同过滤算法实现电影推荐（附源码）

谁借莪１个温暖的怀抱￠ 2023-09-30 15:32 15阅读 0赞

**数据集请点赞收藏关注后评论区留言并且私信博主要**

本例中使用得是著名得电影数据集MovieLens-100数据集

MoviesLens数据集是实现和测试电影推荐最常用得数据集之一，包含943个用户为精选得1682部电影给出得100000个电影评分

主要文件如下**1:u.data 2:u.item 3:u.user**

**1：查看用户/电影排名信息得代码如下**

**![7fa6139cac8b40babc785728e92a9d12.png][]**

import  pandas as pd
    heads=['user_id','item_id','rating','timestamp']
    ratings=pd.read_csv(r'u.data',sep='\t',names=heads)
    print(ratings)
    print("用户数量",len(ratings))

**2：查看导入的电影数据表**

**![fca126f266b34b97abf8783bdb6c7cf0.png][]**

![a7429c97f50d4742940e70c905618a10.png][]

代码如下

import  pandas as pd
    u_cols=['user_id','age','sex','occupation','zip_code']
    users=pd.read_csv(r'u.data',sep='|',names=u_cols,encoding='latin-1')
    print(users)
    r_cols=['user_id','movie_id','rating','unix_timestamp']
    ratings=pd.read_csv(r'u.data',sep='\t',names=r_cols,encoding='latin-1')
    print(ratings)
    m_cols=['movie_id','title','release_data','video_release_data','imdb_url']
    movies=pd.read_csv(r'u.item',sep='|',names=m_cols,usecols=range(5),encoding='latin-1')
    print(movies)

**3：用协同过滤推荐算法进行电影推荐**

![56ffa9b6d5f3427b954f7933e9fc5516.png][]

![c514ad4b0f7646d1ad4dbeb7548f6aa2.png][]

![f0bc332e4f444deaa0e4b070eed87238.png][]

**误差评估如下**

**![73632ee7207c44c7bcc2f0bcf3dc20fb.png][]**

**全部代码如下**

import  pandas as pd
    import  numpy as np
    from sklearn.metrics.pairwise import pairwise_distances
    np.set_printoptions(suppress=True)  # 取消科学计数法输出
    pd.set_option('display.max_rows', None) # 展示所有行
    pd.set_option('display.max_columns', None) # 展示所有列
    def predict(scoredata,similarity,type='user'):
        #基于物品得推荐
        if type=='item':
            predt_mat=scoredata.dot(similarity)/np.array([np.abs(similarity).sum(axis=1)])
        elif type=='user':
            #计算用户评分值 减少用户评分高低习惯影响
            user_meanscorse=scoredata.mean(axis=1)
            score_diff=(scoredata-user_meanscorse.reshape(-1,1))
            predt_mat=user_meanscorse.reshape(-1,1)+similarity.dot(score_diff)/np.array([np.abs(similarity).sum(axis=1)]).T
        return predt_mat
    
    #读取数据
    print('step 1 读取数据')
    r_cols=['user_id','movie_id','rating','unix_timestamp']
    scoredata=pd.read_csv(r'u.data',sep='\t',names=r_cols,encoding='latin-1')
    print('数据形状',scoredata.shape)
    #生成用户-物品评分矩阵
    print('step2 生成 用户物品评分矩阵')
    n_users=943
    n_items=1682
    data_matrix=np.zeros((n_users,n_items))
    for line in range(np.shape(scoredata)[0]):
        row=scoredata['user_id'][line]-1
        col=scoredata['movie_id'][line]-1
        score=scoredata['rating'][line]
        data_matrix[row,col]=score
    print('用户物品矩阵形状',data_matrix.shape)
    #计算相似度
    print('step3 计算相似度')
    user_similaritry=pairwise_distances(data_matrix,metric='cosine')
    item_similarity=pairwise_distances(data_matrix.T,metric='cosine')
    print('user similarity',user_similaritry.shape)
    print('item similartity',item_similarity.shape)
    #进行相似度进行预测
    print('step4 预测')
    user_prediction=predict(data_matrix,user_similaritry,type='user')
    item_perdiction=predict(data_matrix,item_similarity,type='item')
    
    #显示推荐结果
    print('step 5 显示推荐结果')
    print('----------------')
    print('ubcf预测形状',user_prediction.shape)
    print('real answer\n',data_matrix[:5,5])
    print('预测结果\n',user_prediction)
    print('ibcf预测形状',item_perdiction.shape)
    print('real answer\n',data_matrix[:5,:5])
    print('预测结果\n',item_perdiction)
    #性能评估
    print('step 6 性能评估')
    from sklearn.metrics import mean_squared_error
    from math import sqrt
    def rmse(predct,realNum):
        predct=predct[realNum.nonzero()].flatten()
        realNum=realNum[realNum.nonzero()].flatten()
        return sqrt(mean_squared_error(predct,realNum))
    print('u-base mse=',str(rmse(user_prediction,data_matrix)))
    print('m-based mse=',str(rmse(item_perdiction,data_matrix)))

**数据集请点赞收藏关注后私信博主要**

[7fa6139cac8b40babc785728e92a9d12.png]: https://img-blog.csdnimg.cn/7fa6139cac8b40babc785728e92a9d12.png
[fca126f266b34b97abf8783bdb6c7cf0.png]: https://img-blog.csdnimg.cn/fca126f266b34b97abf8783bdb6c7cf0.png
[a7429c97f50d4742940e70c905618a10.png]: https://img-blog.csdnimg.cn/a7429c97f50d4742940e70c905618a10.png
[56ffa9b6d5f3427b954f7933e9fc5516.png]: https://img-blog.csdnimg.cn/56ffa9b6d5f3427b954f7933e9fc5516.png
[c514ad4b0f7646d1ad4dbeb7548f6aa2.png]: https://img-blog.csdnimg.cn/c514ad4b0f7646d1ad4dbeb7548f6aa2.png
[f0bc332e4f444deaa0e4b070eed87238.png]: https://img-blog.csdnimg.cn/f0bc332e4f444deaa0e4b070eed87238.png
[73632ee7207c44c7bcc2f0bcf3dc20fb.png]: https://img-blog.csdnimg.cn/73632ee7207c44c7bcc2f0bcf3dc20fb.png