机器学习 — 提供推荐
提供推荐
- 计算两个人的相似度
- 本来是推荐平均评分较高的作品，考虑到两个人的爱好相似程度，对评分根据相似度进行加权平均
计算相似度：
- 欧几里得距离
- pearson相关度
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,
'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}}计算相关度
pearson相关系数计算公式（参考）
from math import sqrt
# 欧几里得距离评价
def sim_distance(prefs, person1, person2):
si = {}
for item in prefs[person1]:
if item in prefs[person2]:
si[item] = 1
if len(si) == 0:
return 0
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 + sqrt(sum_of_squares))
# 皮尔逊相关度评价
def sim_pearson(prefs, person1, person2):
# 得到两者评价过的相同商品
si = {}
for item in prefs[person1]:
if item in prefs[person2]:
si[item] = 1
n = len(si)
# 如果两个用户之间没有相似之处则返回1
if n == 0:
return 1
# 对各自的所有偏好求和
sum1 = sum([prefs[person1][item] for item in si])
sum2 = sum([prefs[person2][item] for item in si])
# 求各自的平方和
sum1_square = sum([pow(prefs[person1][item], 2) for item in si])
sum2_square = sum([pow(prefs[person2][item], 2) for item in si])
# 求各自的乘积的平方
sum_square = sum([prefs[person1][item] * prefs[person2][item] for item in si])
# 计算pearson相关系数
den = sqrt((sum1_square - pow(sum1, 2) / n) * (sum2_square - pow(sum2, 2) / n))
if den == 0:
return 0
return (sum_square - (sum1 * sum2/n)) / den
print sim_distance(critics, 'Lisa Rose', 'Gene Seymour')0.294298055086print sim_pearson(critics, 'Lisa Rose', 'Gene Seymour')0.396059017191评论者打分
def topMatches(prefs, person, n = 5, simlarity = sim_pearson):
scores = [(simlarity(prefs, person, other), other) for other in prefs if other != person]
# 对列表进行排序，评价高者排在前面
scores.sort()
scores.reverse()
# 取指定个数的（不需要判断n的大小，因为python中的元组可以接受正、负不在范围内的index）
return scores[0:n]寻找和“Toby”有相似偏好的人，取前3个
topMatches(critics, 'Toby', n = 3)[(0.9912407071619299, 'Lisa Rose'),
(0.9244734516419049, 'Mick LaSalle'),
(0.8934051474415647, 'Claudia Puig')]# 利用其他所有人的加权平均给用户推荐
def get_recommendations(prefs, person, similarity=sim_pearson):
# 其他用户对某个电影的评分加权之后的总和
totals = {}
# 其他用户的相似度之和
sim_sums = {}
for other in prefs:
# 不和自己比较
if other == person:
continue
# 求出相似度
sim = similarity(prefs, person, other)
# 忽略相似度小于等于情况0的
if sim <= 0:
continue
# 获取other所有的评价过的电影评分的加权值
for item in prefs[other]:
# 只推荐用户没看过的电影
if item not in prefs[person] or prefs[person][item] == 0:
#print item
# 设置默认值
totals.setdefault(item, 0)
# 求出该电影的加权之后的分数之和
totals[item] += prefs[other][item] * sim
# 求出各个用户的相似度之和
sim_sums.setdefault(item, 0)
sim_sums[item] += sim
# 对于加权之后的分数之和取平均值
rankings = [(total / sim_sums[item], item) for item, total in totals.items()]
# 返回经过排序之后的列表
rankings.sort()
rankings.reverse()
return rankings
给出Toby的电影推荐列表
print get_recommendations(critics, 'Toby')
print get_recommendations(critics, 'Toby', similarity=sim_distance)[(3.3477895267131013, 'The Night Listener'), (2.8325499182641614, 'Lady in the Water'), (2.5309807037655645, 'Just My Luck')]
[(3.457128694491423, 'The Night Listener'), (2.778584003814924, 'Lady in the Water'), (2.4224820423619167, 'Just My Luck')]