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