集体智慧编程（2）——提供推荐

第二章 提供推荐

摘要：本章将主要讲述，如何根据群体中的个人兴趣偏爱来为其他人提供推荐。

协作性过滤

对一大群人进行搜索，并从中找出与你的品味相似的人

搜集偏好

构造数据集

# A dictionary of movie critics and their ratings of a small set of movies
critics = {'Lisa Rose': {'Lady in the Water': 2.5, 'Snakes on a Plane': 3.5,
'Just My Luck': 3.0, 'Superman Returns': 3.5, 'You, Me and Dupree': 2.5,
'The Night Listener': 3.0},
'Gene Seymour': {'Lady in the Water': 3.0, 'Snakes on a Plane': 3.5,
'Just My Luck': 1.5, 'Superman Returns': 5.0, 'The Night Listener': 3.0,
'You, Me and Dupree': 3.5},
'Michael Phillips': {'Lady in the Water': 2.5, 'Snakes on a Plane': 3.0<
4000
/span>,
'Superman Returns': 3.5, 'The Night Listener': 4.0},
'Claudia Puig': {'Snakes on a Plane': 3.5, 'Just My Luck': 3.0,
'The Night Listener': 4.5, 'Superman Returns': 4.0,
'You, Me and Dupree': 2.5},
'Mick LaSalle': {'Lady in the Water': 3.0, 'Snakes on a Plane': 4.0,
'Just My Luck': 2.0, 'Superman Returns': 3.0, 'The Night Listener': 3.0,
'You, Me and Dupree': 2.0},
'Jack Matthews': {'Lady in the Water': 3.0, 'Snakes on a Plane': 4.0,
'The Night Listener': 3.0, 'Superman Returns': 5.0, 'You, Me and Dupree': 3.5},
'Toby': {'Snakes on a Plane': 4.5, 'You, Me and Dupree': 1.0, 'Superman Returns': 4.0}}

查询、修改的测试语句

python  # 打开python解释器
from recommendations import critics #   将critics加载到内存中，但一般更通常的做法是将其存入一个数据库中
critics['Lisa Rose']['Lady in the Water']   #   查询Lisa Rose对Lady in the Water的评分
critics['Toby']['Snakes on a Plane']=4.6    # 修改'Snakes on a Plane'由4.5到4.6
critics['Toby']     #查询Toby的所有电影评分

需找相似的用户

欧几里德距离评价

思想：以经过人们一致评价的物品作为坐标轴，将参与评价的人绘制到图上，考查他们彼此间的距离远近。距离越近，则二者的相似度越高。



在recommendnations.py中加入以下语句

from math import sqrt

# 返回person1和person2基于距离的相似度评价
def sim_distance(prefs, person1, person2):
# 得到二者一致评价的物品的列表
si = {}
for item in prefs[person1]:
if item in prefs[person2]: si[item] = 1

# 如果二者没有一致评价的物品，则返回0
if len(si) == 0: return 0

# 计算所有差值的平方和
sum_of_squares = sum([pow(prefs[person1][item] - prefs[person2][item], 2)
for item in prefs[person1] if item in prefs[person2]])

return 1 / (1 + sum_of_squares)

给出Lisa Rose和Gene Seymour的相似度评价

import recommendations
import importlib
importlib.reload(recommendations)   #   python3中的reload用法
recommendations.sim_distance(recommendations.critics,'Lisa Rose','Gene Seymour')    #   retrun 0.148148.... （此处中文版的书中有误）

皮尔逊相关度评价 （实际上就是概率论里学的相关系数，等于X、Y的协方差除以X、Y标准差的乘积）

思想：以参与评价的人作为坐标轴，将一致评价的物品绘制到图上，拟合出一条直线，尽可能的经过（靠近）多的点。通过比较两组数据与拟合直线的拟合度，来决定二者的相似度。

# 返回p1和p2的皮尔逊相关系数
def sim_pearson(prefs, p1, p2):
# 得到二者一致评价的物品（偏好）的列表
si = {}
for item in prefs[p1]:
if item in prefs[p2]: si[item] = 1

# 如果二者没有一致评价的物品（偏好），则返回0
if len(si) == 0: return 0

# 一致评价的物品总数
n = len(si)

# 所有偏好求和
sum1 = sum([prefs[p1][it] for it in si])
sum2 = sum([prefs[p2][it] for it in si])

# 所有偏好求平方和
sum1Sq = sum([pow(prefs[p1][it], 2) for it in si])
sum2Sq = sum([pow(prefs[p2][it], 2) for it in si])

# 求乘积和
pSum = sum([prefs[p1][it] * prefs[p2][it] for it in si])

# 计算皮尔逊相关系数r
num = pSum - (sum1 * sum2 / n)
den = sqrt((sum1Sq - pow(sum1, 2) / n) * (sum2Sq - pow(sum2, 2) / n))
if den == 0: return 0

r = num / den

return r

import recommendations
import importlib
importlib.reload(recommendations)
recommendations.sim_pearson(recommendations.critics,'Lisa Rose','Gene Seymour')
#   return 0.39605901719066977

为评论者打分

# 从偏好字典中返回最佳匹配者
# 返回结果个数和相似度函数均为可选参数
def topMatches(prefs,person,n=5,similarity=sim_pearson):
scores=[(similarity(prefs,person,other),other)
for other in prefs if other!=person]
# 对评论进行排序，由高到低
scores.sort()
scores.reverse()
return scores[0:n]

import recommendations
import importlib
importlib.reload(recommendations)
recommendations.topMatches(recommendations.critics,'Toby',n=3)
#   return [(0.9912407071619299, 'Lisa Rose'), (0.9244734516419049, 'Mick LaSalle'), (0.8934051474415647, 'Claudia Puig')]

推荐物品

# 利用其他所有人的加权平均，为某人提供建议
def getRecommendations(prefs, person, similarity=sim_pearson):
totals = {}
simSums = {}
for other in prefs:
# 不要和自己作比较
if other == person: continue
sim = similarity(prefs, person, other)

# 忽略不大于0的相似度评分
if sim <= 0: continue
for item in prefs[other]:

# 仅为我没有看过的电影评分
if item not in prefs[person] or prefs[person][item] == 0:
# 相似度*评分
totals.setdefault(item, 0)
totals[item] += prefs[other][item] * sim
# 参与评分的人的相似度求和
simSums.setdefault(item, 0)
simSums[item] += sim

# 建立归一化列表
rankings = [(total / simSums[item], item) for item, total in totals.items()]

# 排序，返回
rankings.sort()
rankings.reverse()
return rankings

import recommendations
import importlib
importlib.reload(recommendations)
recommendations.getRecommendations(recommendations.critics,'Toby')
#  return [(3.3477895267131013, 'The Night Listener'), (2.8325499182641614, 'Lady in the Water'), (2.530980703765565, 'Just My Luck')]

也可以指定使用欧几里德距离评价方式

recommendations.getRecommendations(recommendations.critics,'Toby',similarity=recommendations.sim_distance)
#   return [(3.5002478401415877, 'The Night Listener'), (2.7561242939959363, 'Lady in the Water'), (2.461988486074374, 'Just My Luck')]

匹配商品

将人与物品对调，就可以复用之前写的方法了。

def transformPrefs(prefs):
result={}
for person in prefs:
for item in prefs[person]:
result.setdefault(item,{})

# 将物品与人交换
result[item][person]=prefs[person][item]
return result

import recommendations
import importlib
importlib.reload(recommendations)
movies=recommendations.tr
e71c
ansformPrefs(recommendations.critics)
recommendations.topMatches(movies,'Superman Returns')
#   [(0.6579516949597695, 'You, Me and Dupree'), (0.4879500364742689, 'Lady in the Water'), (0.11180339887498941, 'Snakes on a Plane'), (-0.1798471947990544, 'The Night Listener'), (-0.42289003161103106, 'Just My Luck')]
#   相关度为负代表有不喜欢、讨厌的倾向

基于物品的过滤

基于用户的协作型过滤

即上述算法

基于物品的协作型过滤

思路：为每件物品预先计算好最为接近的其它物品

优点：不会像用户间的比较那么频繁变化

构造物品比较数据集

def calculateSimilarItems(prefs,n=10):
# 为与这些物品最为相近的其它物品建立字典
result={}
# 转变为以物品为中心
itemPrefs=transformPrefs(prefs)
c=0
for item in itemPrefs:
# 为大数据集更新状态变量
c+=1
if c%100==0: print "%d / %d" % (c,len(itemPrefs))
# 找出最为接近的物品
scores=topMatches(itemPrefs,item,n=n,similarity=sim_distance)
result[item]=scores
return result

reload(recommendations)
itemsim=recommendations.calculateSimilarItems(recommendations.critics)
itemsim
#   return {
'Lady in the Water': [(0.4, 'You, Me and Dupree'), (0.2857142857142857, 'The Night Listener'), (0.2222222222222222, 'Snakes on a Plane'), (0.2222222222222222, 'Just My Luck'), (0.09090909090909091, 'Superman Returns')],
'Snakes on a Plane': [(0.2222222222222222, 'Lady in the Water'), (0.18181818181818182, 'The Night Listener'), (0.16666666666666666, 'Superman Returns'), (0.10526315789473684, 'Just My Luck'), (0.05128205128205128, 'You, Me and Dupree')],
'Just My Luck': [(0.2222222222222222, 'Lady in the Water'), (0.18181818181818182, 'You, Me and Dupree'), (0.15384615384615385, 'The Night Listener'), (0.10526315789473684, 'Snakes on a Plane'), (0.06451612903225806, 'Superman Returns')],
'Superman Returns': [(0.16666666666666666, 'Snakes on a Plane'), (0.10256410256410256, 'The Night Listener'), (0.09090909090909091, 'Lady in the Water'), (0.06451612903225806, 'Just My Luck'), (0.05333333333333334, 'You, Me and Dupree')],
'You, Me and Dupree': [(0.4, 'Lady in the Water'), (0.18181818181818182, 'Just My Luck'), (0.14814814814814814, 'The Night Listener'), (0.05333333333333334, 'Superman Returns'), (0.05128205128205128, 'Snakes on a Plane')],
'The Night Listener': [(0.2857142857142857, 'Lady in the Water'),(0.18181818181818182, 'Snakes on a Plane'), (0.15384615384615385, 'Just My Luck'), (0.14814814814814814, 'You, Me and Dupree'), (0.10256410256410256, 'Superman Returns')]
}

获得推荐

类似于上面的“推荐物品”中的算法

def getRecommendedItems(prefs,itemMatch,user):
userRatings=prefs[user]
scores={}
totalSim={}
# 循环遍历用户评分过的物品
for (item,rating) in userRatings.items( ):

# 循环遍历与当前物品相似的物品
for (similarity,item2) in itemMatch[item]:

# 忽略用户已经评价过的物品
if item2 in userRatings: continue
# 当前物品的评分与相似物品相似度的加权和
scores.setdefault(item2,0)
scores[item2]+=similarity*rating
# 相似物品的相似度求和
totalSim.setdefault(item2,0)
totalSim[item2]+=similarity

# 加权和除以相似度和，求出归一化评分
rankings=[(score/totalSim[item],item) for item,score in scores.items( )]

# Return the rankings from highest to lowest
rankings.sort( )
rankings.reverse( )
return rankings

reload(recommendations)
recommendations.getRecommendedItems(recommendations.critics,itemsim,'Toby')
#   return [(3.182634730538922, 'The Night Listener'), (2.5983318700614575, 'Just My Luck'), (2.4730878186968837, 'Lady in the Water')]

使用movielens数据集

def loadMovieLens(path='D:/Documents/PycharmProjects/jtzhbc/chapter2'):
# 获取影片标题
movies = {}
for line in open(path + '/u.item'):
(id, title) = line.split('|')[0:2]
movies[id] = title

# 加载数据
prefs = {}
for line in open(path + '/u.data'):
(user, movieid, rating, ts) = line.split('\t')
prefs.setdefault(user, {})
prefs[user][movies[movieid]] = float(rating)
return prefs

生成数据集

reload(recommendations)
prefs=recommendations.loadMovieLens( )

查看任意一位用户的评分情况

prefs['87']
#   return {'Birdcage, The (1996)': 4.0, 'E.T. the Extra-Terrestrial (1982)': 3.0,
'Bananas (1971)': 5.0, 'Sting, The (1973)': 5.0, 'Bad Boys (1995)': 4.0,
'In the Line of Fire (1993)': 5.0, 'Star Trek: The Wrath of Khan (1982)': 5.0,
'Speechless (1994)': 4.0, etc...

基于用户的推荐

recommendations.getRecommendations(prefs,'87')[0:30]
#   return [(5.0, 'They Made Me a Criminal (1939)'), (5.0, 'Star Kid (1997)'),
(5.0, 'Santa with Muscles (1996)'), (5.0, 'Saint of Fort Washington (1993)'),
etc...]

基于物品的推荐

itemsim=recommendations.calculateSimilarItems(prefs,n=50)   # 生成物品相似度字典的过程可能比较耗时，但当生成以后的推荐耗时则不会再随着用户的增加而增加
recommendations.getRecommendedItems(prefs,itemsim,'87')[0:30]
# return [(5.0, "What's Eating Gilbert Grape (1993)"), (5.0, 'Vertigo (1958)'),
(5.0, 'Usual Suspects, The (1995)'), (5.0, 'Toy Story (1995)'),etc...]

完整的python代码可以在我的github地址上找到：https://github.com/GreenLight74110/PCI