参考网络上的部分资料，做了个最简单的推荐系统的demo实例。
我们将使用MovieLens数据集，它是在实现和测试推荐引擎时所使用的最常见的数据集之一，包含来自943个用户以及精选的1682部电影的评分。
数据的下载地址：ml-100k
直接上代码，具体的说明在注释里。
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 |
#!/usr/bin/env python #coding:utf-8 import numpy as np import pandas as pd import time from sklearn import cross_validation as cv from sklearn.metrics.pairwise import pairwise_distances from sklearn.metrics import mean_squared_error from math import sqrt def read_file(): header = ['user_id', 'item_id', 'rating', 'timestamp'] df = pd.read_csv("/Users/lei.wang/data/ml-100k/u.data",sep = '\t',names = header) #去重之后得到一个元祖，分别表示行与列,大小分别为943与1682 n_users = df.user_id.unique().shape[0] n_items = df.item_id.unique().shape[0] print 'all users is :' + str(n_users) + ', all items is :' + str(n_items) #将样本分为训练集与测试机 train_data,test_data = cv.train_test_split(df,test_size = 0.25) train_data_matrix = np.zeros((n_users,n_items)) for line in train_data.itertuples(): train_data_matrix[line[1]-1, line[2]-1] = line[3] test_data_matrix = np.zeros((n_users,n_items)) for line in test_data.itertuples(): test_data_matrix[line[1]-1,line[2]-1] = line[3] #计算user相似矩阵与item相似矩阵,大小分别为943*943,1682*1682 user_similar = pairwise_distances(train_data_matrix, metric = "cosine") item_similar = pairwise_distances(train_data_matrix.T, metric = "cosine") return (train_data_matrix,test_data_matrix,user_similar,item_similar) train_data_matrix,test_data_matrix,user_similar,item_similar = read_file() print 'user_similar.shape is :',user_similar.shape print 'item_similar.shape is :',item_similar.shape def predict(rating, similar, type = 'user'): if type == 'user': mean_user_rating = rating.mean(axis = 1) rating_diff = (rating - mean_user_rating[:,np.newaxis]) pred = mean_user_rating[:,np.newaxis] + similar.dot(rating_diff) / np.array([np.abs(similar).sum(axis=1)]).T elif type == 'item': pred = rating.dot(similar) / np.array([np.abs(similar).sum(axis=1)]) return pred user_prediction = predict(train_data_matrix, user_similar, type = 'user') item_prediction = predict(train_data_matrix, item_similar, type = 'item') def rmse(prediction,ground_truth): prediction = prediction[ground_truth.nonzero()].flatten() ground_truth = ground_truth[ground_truth.nonzero()].flatten() return sqrt(mean_squared_error(prediction, ground_truth)) print 'User based CF RMSE: ' + str(rmse(user_prediction, test_data_matrix)) print 'Item based CF RMSe: ' + str(rmse(item_prediction, test_data_matrix)) |
文章来源：bitcarmanlee
