基于协同过滤算法的推荐系统实现

# -*- coding: cp936 -*-
#基于协同过滤的推荐系统
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,'You,Me and Dupree':3.5,'The Night Listener':3.0},
         'Michael Phillips':{'Lady in the Water':2.5,'Snakes on a Plane':3.0,'Just My Luck':3.0,
                      'Superman Returns':3.5,'You,Me and Dupree':2.5,'The Night Listener':4.0},
         'Claudia Puig':{'Lady in the Water':2.5,'Snakes on a Plane':3.5,'Just My Luck':3.0,
                      'Superman Returns':4.0,'You,Me and Dupree':2.5,'The Night Listener':4.5},
         'Toby':{'Snakes on a Plane':4.5,'Superman Returns':4.0,'You,Me and Dupree':1.0}}

from math import sqrt

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))

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
    sum1=sum([prefs[p1][it] for it in si])
    sum2=sum([prefs[p2][it] for it in si])

    sum1Sq=sum([pow(prefs[p1][it],2) for it in si])
    sum2Sq=sum([pow(prefs[p2][it],2) for it in si])

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

    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

def topMatches(prefs,person,n=5,similarity=sim_pearson):
    scores=[(similarity(prefs,person,other),other) for other in prefs if other!=person]

    scores.sort()
    scores.reverse()
    return scores[0:n]

def getRecommendations(prefs,person,similarity=sim_pearson):
    totals={}
    simSums={}
    for other in prefs:
        if other==person:
            continue
        sim=similarity(prefs,person,other)

        if sim<=0:
            continue
        for item in prefs[other]:
            if item not in prefs[person] or prefs[person][item]==0:
                totals.setdefault(item,0)
                totals[item]+=prefs[other][item]*sim

                simSums.setdefault(item,0)
                simSums[item]+=sim

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

    rankings.sort()
    rankings.reverse()
    return rankings