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60 行
1.8 KiB
60 行
1.8 KiB
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#import warnings
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#warnings.filterwarnings("ignore")
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import pandas as pd
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import numpy as np
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#获取数据
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ratings_df = pd.read_csv('real_ratings.csv')
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movies_df = pd.read_csv('movies.csv')
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userNo = max(ratings_df['userId'])+1
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movieNo = max(ratings_df['movieRow'])+1
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print(userNo,movieNo)
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#创建电影评分表
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rating = np.zeros((userNo,movieNo))
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for index,row in ratings_df.iterrows():
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rating[int(row['userId']),int(row['movieRow'])]=row['rating']
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def recommend(userID,lr,alpha,d,n_iter,data):
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'''
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userID(int):推荐用户ID
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lr(float):学习率
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alpha(float):权重衰减系数
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d(int):矩阵分解因子(即元素个数)
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n_iter(int):训练轮数
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data(ndarray):用户-电影评分矩阵
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'''
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#获取用户数与电影数
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m,n = data.shape
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#初始化参数
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x = np.random.uniform(0,1,(m,d))
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w = np.random.uniform(0,1,(d,n))
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#创建评分记录表，无评分记为0，有评分记为1
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record = np.array(data>0,dtype=int)
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#梯度下降，更新参数
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for i in range(n_iter):
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x_grads = np.dot(np.multiply(record,np.dot(x,w)-data),w.T)
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w_grads = np.dot(x.T,np.multiply(record,np.dot(x,w)-data))
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x = alpha*x - lr*x_grads
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w = alpha*w - lr*w_grads
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#预测
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predict = np.dot(x,w)
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#将用户未看过的电影分值从低到高进行排列
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for i in range(n):
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if record[userID-1][i] == 1 :
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predict[userID-1][i] = 0
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recommend = np.argsort(predict[userID-1])
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a = recommend[-1]
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b = recommend[-2]
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c = recommend[-3]
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d = recommend[-4]
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e = recommend[-5]
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print('为用户%d推荐的电影为：\n1:%s\n2:%s\n3:%s\n4:%s\n5:%s。'\
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%(userID,movies_df['title'][a],movies_df['title'][b],movies_df['title'][c],movies_df['title'][d],movies_df['title'][e]))
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recommend(123,1e-4,0.999,20,100,rating)
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