python3之协同过滤综述

协同过滤主要是计算人与人之间的距离，Python缔造的recommendations函数如下

####数据集（用户，电影名字，评分）

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}}



###计算举例的函数（人与人之间相似度）



​def sim_pearson(prefs,p1,p2):



  # Get the list of mutually rated items



  si={}



  for item in prefs[p1]: 



    if item in prefs[p2]: si[item]=1



  # if they are no ratings in common, return 0



  if len(si)==0: return 0



  # Sum calculations



  n=len(si)



  



  # Sums of all the preferences



  sum1=sum([prefs[p1][it] for it in si])



  sum2=sum([prefs[p2][it] for it in si])



  



  # Sums of the squares



  sum1Sq=sum([pow(prefs[p1][it],2) for it in si])



  sum2Sq=sum([pow(prefs[p2][it],2) for it in si])



  



  # Sum of the products



  pSum=sum([prefs[p1][it]*prefs[p2][it] for it in si])



  



  # Calculate r (Pearson score)



  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



####推荐方法



def getRecommendations(prefs,person,similarity=sim_pearson):



  totals={}



  simSums={}



  for other in prefs:



    # don't compare me to myself



    if other==person: continue



    sim=similarity(prefs,person,other)



    # ignore scores of zero or lower



    if sim<=0: continue



    for item in prefs[other]:



   



      # only score movies I haven't seen yet



      if item not in prefs[person] or prefs[person][item]==0:



        # Similarity * Score



        totals.setdefault(item,0)



        totals[item]+=prefs[other][item]*sim



        # Sum of similarities



        simSums.setdefault(item,0)



        simSums[item]+=sim



  # Create the normalized list



  rankings=[(total/simSums[item],item) for item,total in totals.items()]



  # Return the sorted list



  rankings.sort()



  rankings.reverse()



  return rankings

​调用方法

import recommendations

t=recommendations.getRecommendations(recommendations.critics,'Toby')

print (t)