基于Python实现的推荐系统实践代码 含数据源和源代码.rar

# -*- coding: utf-8 -*- import pandas as pd import numpy as np from math import sqrt 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}} df_critics=pd.DataFrame(critics) ##欧氏距离 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)) ##numpy pandas 方法 def sim_distance2(prefs,person1,person2): return 1/(1+np.linalg.norm(prefs[person1]-prefs[person2])) ##皮尔逊相关系数 def sim_pearson(prefs,p1,p2): si={} for item in prefs[p1]: if item in prefs[p2]: si[item]=1 n=len(si) if n==0: return 1 ##对所有偏好求和 sum1=sum([prefs[p1][it] for it in si]) sum2=sum([prefs[p2][it] for it in si]) ##求平方和 sum1Sq=sum([pow(prefs[p1][it]