文章主要介绍的是koren 08年发的论文[1],  2.3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

  1. 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 ...

  2. 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 ...

  3. 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 ...

  4. 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 ...

  5. 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& ...

  6. 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 ...

  7. 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 ...

  8. 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 ...

  9. 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

  1. Vagrant安装步骤

    Vagrant安装步骤 下载添加box镜像 vagrant box add base 远端的box地址或者本地的box文件名 建立box镜像关联 vagrant box add centos72 va ...

  2. SUBTRACT

    SUBTRACT 给出一个长度为n序列\(\{a_i\}\),定义一个操作,记做\(con(a,i)\),意思是用\(a_i-a_{i+1}\)替代\(a_i,a_{i+1}\),显然最后一个数字不能 ...

  3. Java迷宫代码，广度优先遍历，最短路径

    使用一个队列,采用层层扩张的方式,寻找迷宫最优的路径信息,再用一个迷宫节点数组记录行走信息方向常量定义: public interface Constant { // 右方向 int RIGHT = ...

  4. Java 基础 - 装箱， 拆箱

    总结 1-装箱过程是通过调用包装器的valueOf方法实现的,而拆箱过程是通过调用包装器的 xxxValue方法实现的.(xxx代表对应的基本数据类型).例如:在装箱的时候自动调用的是Integer的 ...

  5. 校园商铺-4店铺注册功能模块-1Dao层之更新店铺

    dao层增加更新店铺的方法 package com.csj2018.o2o.dao; import com.csj2018.o2o.entity.Shop; public interface Shop ...

  6. VS2010-MFC（Ribbon界面开发：创建Ribbon样式的应用程序框架）

    转自:http://www.jizhuomi.com/software/251.html 上一节讲了GDI对象之画刷CBrush,至此图形图像的入门知识就讲完了.从本节开始将为大家带来Ribbon界面 ...

  7. SpringBoot：目录

    ylbtech-SpringBoot:目录 1.返回顶部   2.返回顶部   3.返回顶部   4.返回顶部   5.返回顶部     6.返回顶部   作者:ylbtech出处:http://yl ...

  8. 搞笑：我是我们村唯一一个搞IT的！

    出自:http://codebay.cn/post/8033.html 当我跟村里人提起我是IT工程师时 他们内心是这样想的 ▼ 也有的朋友这样想 ▼ 爸爸问我,什么狮? ▼ 妈妈问我,工什么? ▼ ...

  9. 《Python之BMI计算》

    <Python之BMI计算> 前段时间写了个 BMI 因为刚刚开始学 有几个错误 第一个: 厘米我当时也没注意因为觉得去掉0.00的话后面1866666666是正确的BMI值 刚刚去看看去 ...

  10. python Six 模块

    Six模块用于python2和python3的兼容 import six 官网链接:https://six.readthedocs.io/