基于baseline、svd和stochastic gradient descent的个性化推荐系统
koren论文中用到netflix 数据集, 过于大, 在普通的pc机上运行时间很长很长。考虑到写文章目地主要是已介绍总结方法为主,所以采用Movielens 数据集。
变量介绍
部分变量介绍可以参看《基于baseline和stochastic gradient descent的个性化推荐系统》
文章中,将介绍两种方法实现的简易个性化推荐系统,用RMSE评价标准,对比这两个方法的实验结果。
(1) svd + stochstic gradient descent 方法来实现系统。
(2) baseline + svd + stochastic gradient descent 方法来实现系统。
注:
方法1: svd + stochastic gradient descent
svd:
cost function:
梯度变化(利用stochastic gradient descent算法使上述的目标函数值,在设定的迭代次数内,降到最小)
具体代码实现:
'''''
Created on Dec 13, 2012 @Author: Dennis Wu
@E-mail: hansel.zh@gmail.com
@Homepage: http://blog.csdn.net/wuzh670 Data set download from : http://www.grouplens.org/system/files/ml-100k.zip
''' from operator import itemgetter, attrgetter
from math import sqrt
import random def load_data(): train = {}
test = {}
filename_train = 'data/ua.base'
filename_test = 'data/ua.test' for line in open(filename_train):
(userId, itemId, rating, timestamp) = line.strip().split('\t')
train.setdefault(userId,{})
train[userId][itemId] = float(rating) for line in open(filename_test):
(userId, itemId, rating, timestamp) = line.strip().split('\t')
test.setdefault(userId,{})
test[userId][itemId] = float(rating) return train, test def calMean(train):
stat = 0
num = 0
for u in train.keys():
for i in train[u].keys():
stat += train[u][i]
num += 1
mean = stat*1.0/num
return mean def initialFeature(feature, userNum, movieNum): random.seed(0)
user_feature = {}
item_feature = {}
i = 1
while i < (userNum+1):
si = str(i)
user_feature.setdefault(si,{})
j = 1
while j < (feature+1):
sj = str(j)
user_feature[si].setdefault(sj,random.uniform(0,1))
j += 1
i += 1 i = 1
while i < (movieNum+1):
si = str(i)
item_feature.setdefault(si,{})
j = 1
while j < (feature+1):
sj = str(j)
item_feature[si].setdefault(sj,random.uniform(0,1))
j += 1
i += 1
return user_feature, item_feature def svd(train, test, userNum, movieNum, feature, user_feature, item_feature): gama = 0.02
lamda = 0.3
slowRate = 0.99
step = 0
preRmse = 1000000000.0
nowRmse = 0.0 while step < 100:
rmse = 0.0
n = 0
for u in train.keys():
for i in train[u].keys():
pui = 0
k = 1
while k < (feature+1):
sk = str(k)
pui += user_feature[u][sk] * item_feature[i][sk]
k += 1
eui = train[u][i] - pui
rmse += pow(eui,2)
n += 1
k = 1
while k < (feature+1):
sk = str(k)
user_feature[u][sk] += gama*(eui*item_feature[i][sk] - lamda*user_feature[u][sk])
item_feature[i][sk] += gama*(eui*user_feature[u][sk] - lamda**item_feature[i][sk])
k += 1 nowRmse = sqrt(rmse*1.0/n)
print 'step: %d Rmse: %s' % ((step+1), nowRmse)
if (nowRmse < preRmse):
preRmse = nowRmse gama *= slowRate
step += 1 return user_feature, item_feature def calRmse(test, user_feature, item_feature, feature): rmse = 0.0
n = 0
for u in test.keys():
for i in test[u].keys():
pui = 0
k = 1
while k < (feature+1):
sk = str(k)
pui += user_feature[u][sk] * item_feature[i][sk]
k += 1
eui = pui - test[u][i]
rmse += pow(eui,2)
n += 1
rmse = sqrt(rmse*1.0 / n)
return rmse; if __name__ == "__main__": # load data
train, test = load_data()
print 'load data success' # initial user and item feature, respectly
user_feature, item_feature = initialFeature(100, 943, 1682)
print 'initial user and item feature, respectly success' # baseline + svd + stochastic gradient descent
user_feature, item_feature = svd(train, test, 943, 1682, 100, user_feature, item_feature)
print 'svd + stochastic gradient descent success' # compute the rmse of test set
print 'the Rmse of test test is: %s' % calRmse(test, user_feature, item_feature, 100)
方法2:baseline + svd + stochastic gradient descent
baseline + svd:
object function:
梯度变化(利用stochastic gradient descent算法使上述的目标函数值,在设定的迭代次数内,降到最小)
方法2: 具体代码实现
'''''
Created on Dec 13, 2012 @Author: Dennis Wu
@E-mail: hansel.zh@gmail.com
@Homepage: http://blog.csdn.net/wuzh670 Data set download from : http://www.grouplens.org/system/files/ml-100k.zip
''' from operator import itemgetter, attrgetter
from math import sqrt
import random def load_data(): train = {}
test = {}
filename_train = 'data/ua.base'
filename_test = 'data/ua.test' for line in open(filename_train):
(userId, itemId, rating, timestamp) = line.strip().split('\t')
train.setdefault(userId,{})
train[userId][itemId] = float(rating) for line in open(filename_test):
(userId, itemId, rating, timestamp) = line.strip().split('\t')
test.setdefault(userId,{})
test[userId][itemId] = float(rating) return train, test def calMean(train):
stat = 0
num = 0
for u in train.keys():
for i in train[u].keys():
stat += train[u][i]
num += 1
mean = stat*1.0/num
return mean def initialBias(train, userNum, movieNum, mean): bu = {}
bi = {}
biNum = {}
buNum = {} u = 1
while u < (userNum+1):
su = str(u)
for i in train[su].keys():
bi.setdefault(i,0)
biNum.setdefault(i,0)
bi[i] += (train[su][i] - mean)
biNum[i] += 1
u += 1 i = 1
while i < (movieNum+1):
si = str(i)
biNum.setdefault(si,0)
if biNum[si] >= 1:
bi[si] = bi[si]*1.0/(biNum[si]+25)
else:
bi[si] = 0.0
i += 1 u = 1
while u < (userNum+1):
su = str(u)
for i in train[su].keys():
bu.setdefault(su,0)
buNum.setdefault(su,0)
bu[su] += (train[su][i] - mean - bi[i])
buNum[su] += 1
u += 1 u = 1
while u < (userNum+1):
su = str(u)
buNum.setdefault(su,0)
if buNum[su] >= 1:
bu[su] = bu[su]*1.0/(buNum[su]+10)
else:
bu[su] = 0.0
u += 1 return bu,bi def initialFeature(feature, userNum, movieNum): random.seed(0)
user_feature = {}
item_feature = {}
i = 1
while i < (userNum+1):
si = str(i)
user_feature.setdefault(si,{})
j = 1
while j < (feature+1):
sj = str(j)
user_feature[si].setdefault(sj,random.uniform(0,1))
j += 1
i += 1 i = 1
while i < (movieNum+1):
si = str(i)
item_feature.setdefault(si,{})
j = 1
while j < (feature+1):
sj = str(j)
item_feature[si].setdefault(sj,random.uniform(0,1))
j += 1
i += 1
return user_feature, item_feature def svd(train, test, mean, userNum, movieNum, feature, user_feature, item_feature, bu, bi): gama = 0.02
lamda = 0.3
slowRate = 0.99
step = 0
preRmse = 1000000000.0
nowRmse = 0.0 while step < 100:
rmse = 0.0
n = 0
for u in train.keys():
for i in train[u].keys():
pui = 1.0 * (mean + bu[u] + bi[i])
k = 1
while k < (feature+1):
sk = str(k)
pui += user_feature[u][sk] * item_feature[i][sk]
k += 1
eui = train[u][i] - pui
rmse += pow(eui,2)
n += 1
bu[u] += gama * (eui - lamda * bu[u])
bi[i] += gama * (eui - lamda * bi[i])
k = 1
while k < (feature+1):
sk = str(k)
user_feature[u][sk] += gama*(eui*item_feature[i][sk] - lamda*user_feature[u][sk])
item_feature[i][sk] += gama*(eui*user_feature[u][sk] - lamda*item_feature[i][sk])
k += 1 nowRmse = sqrt(rmse*1.0/n)
print 'step: %d Rmse: %s' % ((step+1), nowRmse)
if (nowRmse < preRmse):
preRmse = nowRmse gama *= slowRate
step += 1
return user_feature, item_feature, bu, bi def calRmse(test, bu, bi, user_feature, item_feature, mean, feature): rmse = 0.0
n = 0
for u in test.keys():
for i in test[u].keys():
pui = 1.0 * (mean + bu[u] + bi[i])
k = 1
while k < (feature+1):
sk = str(k)
pui += user_feature[u][sk] * item_feature[i][sk]
k += 1
eui = pui - test[u][i]
rmse += pow(eui,2)
n += 1
rmse = sqrt(rmse*1.0 / n)
return rmse; if __name__ == "__main__": # load data
train, test = load_data()
print 'load data success' # Calculate overall mean rating
mean = calMean(train)
print 'Calculate overall mean rating success' # initial user and item Bias, respectly
bu, bi = initialBias(train, 943, 1682, mean)
print 'initial user and item Bias, respectly success' # initial user and item feature, respectly
user_feature, item_feature = initialFeature(100, 943, 1682)
print 'initial user and item feature, respectly success' # baseline + svd + stochastic gradient descent
user_feature, item_feature, bu, bi = svd(train, test, mean, 943, 1682, 100, user_feature, item_feature, bu, bi)
print 'baseline + svd + stochastic gradient descent success' # compute the rmse of test set
print 'the Rmse of test test is: %s' % calRmse(test, bu, bi, user_feature, item_feature, mean, 100)
实验参数设置:
(gama = 0.02 lamda =0.3)
feature = 100 maxstep = 100 slowRate = 0.99(随着迭代次数增加,梯度下降幅度越来越小)
方法1结果:Rmse of test set : 1.00422938926
方法2结果:Rmse of test set : 0.963661477881
REFERENCES
1.Y. Koren. Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model. Proc. 14th ACM SIGKDD Int. Conf. On Knowledge Discovery and Data Mining (KDD’08), pp. 426–434, 2008.
2. Y.Koren. The BellKor Solution to the Netflix Grand Prize 2009
基于baseline、svd和stochastic gradient descent的个性化推荐系统的更多相关文章
- 基于baseline和stochastic gradient descent的个性化推荐系统
文章主要介绍的是koren 08年发的论文[1], 2.1 部分内容(其余部分会陆续补充上来). koren论文中用到netflix 数据集, 过于大, 在普通的pc机上运行时间很长很长.考虑到写文 ...
- FITTING A MODEL VIA CLOSED-FORM EQUATIONS VS. GRADIENT DESCENT VS STOCHASTIC GRADIENT DESCENT VS MINI-BATCH LEARNING. WHAT IS THE DIFFERENCE?
FITTING A MODEL VIA CLOSED-FORM EQUATIONS VS. GRADIENT DESCENT VS STOCHASTIC GRADIENT DESCENT VS MIN ...
- Stochastic Gradient Descent
一.从Multinomial Logistic模型说起 1.Multinomial Logistic 令为维输入向量; 为输出label;(一共k类); 为模型参数向量: Multinomial Lo ...
- Stochastic Gradient Descent 随机梯度下降法-R实现
随机梯度下降法 [转载时请注明来源]:http://www.cnblogs.com/runner-ljt/ Ljt 作为一个初学者,水平有限,欢迎交流指正. 批量梯度下降法在权值更新前对所有样本汇总 ...
- 机器学习-随机梯度下降(Stochastic gradient descent)
sklearn实战-乳腺癌细胞数据挖掘(博主亲自录制视频) https://study.163.com/course/introduction.htm?courseId=1005269003& ...
- 几种梯度下降方法对比(Batch gradient descent、Mini-batch gradient descent 和 stochastic gradient descent)
https://blog.csdn.net/u012328159/article/details/80252012 我们在训练神经网络模型时,最常用的就是梯度下降,这篇博客主要介绍下几种梯度下降的变种 ...
- Stochastic Gradient Descent收敛判断及收敛速度的控制
要判断Stochastic Gradient Descent是否收敛,可以像Batch Gradient Descent一样打印出iteration的次数和Cost的函数关系图,然后判断曲线是否呈现下 ...
- Gradient Descent 和 Stochastic Gradient Descent(随机梯度下降法)
Gradient Descent(Batch Gradient)也就是梯度下降法是一种常用的的寻找局域最小值的方法.其主要思想就是计算当前位置的梯度,取梯度反方向并结合合适步长使其向最小值移动.通过柯 ...
- 随机梯度下降法(Stochastic gradient descent, SGD)
BGD(Batch gradient descent)批量梯度下降法:每次迭代使用所有的样本(样本量小) Mold 一直在更新 SGD(Stochastic gradientdescent)随机 ...
随机推荐
- Python|读、写Excel文件(三种模块三种方式)
python读写excel的方式有很多,不同的模块在读写的讲法上稍有区别: 用xlrd和xlwt进行excel读写: 用openpyxl进行excel读写: 用pandas进行excel读写: imp ...
- STL之__ type_traits
__type_traits:双底线是说明这是SGI STL内部使用的东西,不在STL标准范围之内.iterator_traits负责萃取迭代器(iterator)的特性.而__type_traits则 ...
- 组合数学——cf1065E
从两端到中间分段,然后累乘即可 #include<bits/stdc++.h> using namespace std; #define mod 998244353 #define max ...
- python相关软件安装流程图解——虚拟机操作——复制虚拟机主机——CentOS-7-x86_64-DVD-1810
请先确保已经安装了虚拟机 python相关软件安装流程图解——虚拟机安装——CentOS-7-x86_64-DVD-1810——CentOS-01下载 https://www.cnblogs.com/ ...
- JLOI 2013 卡牌游戏 bzoj3191
题目描述 N个人坐成一圈玩游戏.一开始我们把所有玩家按顺时针从1到N编号.首先第一回合是玩家1作为庄家.每个回合庄家都会随机(即按相等的概率)从卡牌堆里选择一张卡片,假设卡片上的数字为X,则庄家首先把 ...
- manacher/马拉车常用用法一览
因为manacher算法把原来的字符串扩大了两倍,因此在应用时许多二级结论都非常不直观,现场推出来很麻烦,因此笔者在此做个简单整理,如果发现有错误或者有常用的我没有涉及到的,恳请在下方评论区指出,我会 ...
- php数据结构课程---7、队列实战
php数据结构课程---7.队列实战 一.总结 一句话总结: 注意条件:注意循环的条件(比如while循环打印队列元素时),注意if的条件 把问题想清楚:比如链表操作初次插入元素和后面再插,效果是不一 ...
- php开发面试题---面试常用英语(你能介绍你自己吗?)
php开发面试题---面试常用英语(你能介绍你自己吗?) 一.总结 一句话总结: Could you please describe yourself? 1.为什么觉得自己适合这份工作? Why do ...
- Oracle Spatial导入shp数据
现在开始尝试用oracle spatial管理空间数据,刚学会shp数据的导入,总结如下.oracle11g安装后,已经有了oracle spatial组件,我们只需要用shp2sdo.exe工具,就 ...
- iOS开发之SceneKit框架--SCNAction.h
1.SCNAction简介 主要负责节点SCNNode的属性,实现node的渐变.移动.出现.消失.实现动画等. 2.相关API 节点的移动(earthNode的初始坐标(5,0,0)) //从当前位 ...