python协同过滤算法实现电影推荐（附源码）

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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

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

 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

 

 误差评估如下

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)))

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