recommendations.py

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}}
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+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 0

    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(items,0)
                simSums[item]+=sim
    
    
    rankings=[(total/simSums[item],item) for item ,total in totals.items()]
    
    rankings.sort()
    rankings.reverse()
    return rankings
def transformPrefs(prefs):
    result={}
    for person in prefs[person]:
        result.setdefault(item,{})
        
        result[item][person]=prefs[person][item]
        
    return result
def calculateSimilarItems(perfs,n=10):
    result={}
    
    itemPrefs=transformPrefs(prefs)
    c=0
    for item in itemPrefs:
        c+=1
        if c%100==0: print "%d / %d" %(c,len(itemPrefs))
        scores=topMatches(itemPrefs,item,n=n,similarity=sim_distance)
        result[item]=scores
    return result


def getRecommendedItems(prefs,itemMatch,user):
    userRatings=prefs[user]
    scores={}
    totalSim={}
    
    for (item,rating) in userRatings.items():
        for (similarity,item2) in itemMatch[item]:
            if item2 in userRatings: continue
                
            scores.setdefault(item2,0)
            scores[item2]+=similarity
            
    rankings=[(score/totalSim[item],item) for item,score in scores.items()]
    
    rankings.sort()
    rankings.reverse()
    return rankings