代码实现:
a.数据集
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}}
b.算法实现
import recommenfations
from math import sqrt
# 返回p1和p2的皮尔逊相关系数
def persion_person(prefs, person1, person2):
# 得到双方都曾评价过的物品列表
share_item = {}
for item in prefs[person1]:
if item in prefs[person2]:
share_item[item] = 1
# 得到列表元素的个数
n = len(share_item)
# 如果没用共同之处,则返回1
if n == 0:
return 1
# 对所有偏好求和
sum_person1 = sum([prefs[person1][item] for item in share_item])
sum_person2 = sum([prefs[person2][item] for item in share_item])
# 求平方和
sum_person1_Sq = sum([pow(prefs[person1][item], 2) for item in share_item])
sum_person2_Sq = sum([pow(prefs[person2][item], 2) for item in share_item])
# 求乘积之和 E(XY)
pSum = sum([prefs[person1][item] * prefs[person2][item] for item in share_item])
# 计算皮尔逊评价值
num = pSum - (sum_person1 * sum_person2 / n)
den = sqrt((sum_person1_Sq - pow(sum_person1, 2) / n) * (sum_person2_Sq - pow(sum_person2, 2) / n))
if den == 0:
return 0
r = num / den
return r