oceniPoKorisnici={
    'Lisa Rose': {'Catch Me If You Can': 3.0 , 'Snakes on a Plane': 3.5, 'Superman Returns': 3.5, 'You, Me and Dupree': 2.5, 'The Night Listener': 3.0, 'Snitch': 3.0},
    'Gene Seymour': {'Lady in the Water': 3.0, 'Snakes on a Plane': 3.5, 'Just My Luck': 1.5,  'The Night Listener': 3.0,'You, Me and Dupree': 3.5},
    'Michael Phillips': {'Catch Me If You Can': 2.5, 'Lady in the Water': 2.5,'Superman Returns': 3.5, 'The Night Listener': 4.0, 'Snitch': 2.0},
    '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, 'You, Me and Dupree': 2.0},
    'Jack Matthews': {'Catch Me If You Can': 4.5, '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, 'Snitch': 4.5},
    'Toby': {'Snakes on a Plane':4.5, 'Snitch': 5.0},
    'Michelle Nichols': {'Just My Luck' : 1.0, 'The Night Listener': 4.5, 'You, Me and Dupree': 3.5, 'Catch Me If You Can': 2.5, 'Snakes on a Plane': 3.0},
    'Gary Coleman': {'Lady in the Water': 1.0, 'Catch Me If You Can': 1.5, 'Superman Returns': 1.5, 'You, Me and Dupree': 2.0},
    'Larry': {'Lady in the Water': 3.0, 'Just My Luck': 3.5, 'Snitch': 1.5, 'The Night Listener': 3.5}
    }

from math import sqrt
# Vrakja merka za slicnost bazirana na rastojanieto za person1 i person2
def sim_distance(oceni,person1,person2):
    # Se pravi lista na zaednicki predmeti (filmovi)
    zaednicki={}
    for item in oceni[person1].keys():
        if item in oceni[person2]:
            zaednicki[item]=1
    # ako nemaat zaednicki rejtinzi, vrati 0
    if len(zaednicki)==0:
        return 0
    # Soberi gi kvadratite na site razliki
     # sum_of_squares=sum([pow(oceni[person1][item]-oceni[person2][item],2)
     #     for item in oceni[person1] if item in oceni[person2]])

    sum_of_squares=0
    for item in zaednicki.keys():
        sum_of_squares+=(oceni[person1][item]-oceni[person2][item])**2
    return 1/(1+sqrt(sum_of_squares))
#    return 1/(1+(sum_of_squares))

def sim_pearson(oceni,person1,person2):
    # Se pravi lista na zaednicki predmeti (filmovi)
    zaednicki=[]
    for item in oceni[person1].keys():
        if item in oceni[person2]:
            zaednicki.append(item)
    # ako nemaat zaednicki rejtinzi, vrati 0
    if len(zaednicki)==0:
        return 0

    sumaOceni1=0
    sumaOceni2=0
    sumaOceniKvadrati1=0
    sumaOceniKvadrati2=0
    sumaZaednickiProizvodi=0

    for item in zaednicki:
        ocena1=oceni[person1][item]
        ocena2=oceni[person2][item]
        sumaOceni1+=ocena1
        sumaOceni2+=ocena2
        sumaOceniKvadrati1+=ocena1**2
        sumaOceniKvadrati2+=ocena2**2
        sumaZaednickiProizvodi+=ocena1*ocena2
        # print ocena1,ocena2,item

    n=len(zaednicki)
    broitel=n*sumaZaednickiProizvodi-sumaOceni1*sumaOceni2
    imenitel=(n*sumaOceniKvadrati1-sumaOceni1**2)**0.5 * (n*sumaOceniKvadrati2-sumaOceni2**2)**0.5
    if imenitel==0:
        if broitel>0:
            return 1
        elif broitel<0:
            return -1
        else:
            return -2


    formula=broitel/imenitel
    return formula

def transformPrefs(prefs):
    result={}
    for person in prefs:
        for item in prefs[person]:
            result.setdefault(item,{})
            # Zameni gi mestata na licnosta i predmetot
            result[item][person]=prefs[person][item]
    return result


def topMatches(prefs,person,n=5,similarity=sim_pearson):
    scores=[(similarity(prefs,person,other),other)
                   for other in prefs if other!=person]
    # Se sortira listata vo rastecki redosled
    scores.sort( )
    # Se prevrtuva za najslicnite (so najgolema vrednost) da bidat prvi
    scores.reverse( )
    return scores[0:n]

def getRecommendations(prefs,person,similarity=sim_pearson):
    totals={}
    simSums={}
    for other in prefs:
        # Za da ne se sporeduva so samiot sebe
        if other==person: continue
        sim=similarity(prefs,person,other)
        # ne se zemaat vo predvid rezultati <= 0
        if sim<=0: continue
        for item in prefs[other]:
            # za tekovniot korisnik gi zemame samo filmovite sto gi nemame veke gledano
            if item not in prefs[person] or prefs[person][item]==0:
                # Similarity * Score   (Slicnost * Ocena)
                totals.setdefault(item,0)
                totals[item]+=prefs[other][item]*sim

                # Sumuma na slicnosti
                simSums.setdefault(item,0)
                simSums[item]+=sim

    # Kreiranje na normalizirana lista so rejtinzi
    rankings=[(total/simSums[item],item) for item,total in totals.items()]

    # Sortiranje na listata vo rastecki redosled
    rankings.sort()
    # Prevrtuvanje na lista za najgolemite vrednosti da bidat napred
    rankings.reverse()
    return rankings


def getUserBasedRecomendations(oceni,korisnik,similarity=sim_pearson):
    rankings = []
    slicni=getRecommendations(oceni,korisnik)
    for item in slicni:
        rankings.append(item[1])
    rankings=rankings[0:3]
    rankings.sort()
    return rankings

def getItemBasedRecomendations(oceni,korisnik,similarity=sim_pearson):
    rankings = []
    itemsBased = transformPrefs(oceni)
    movies = oceni[korisnik]
    notWatch = []
    for movie in itemsBased:
        if movie not in movies:
            notWatch.append(movie)
    added={}
    for movie in movies:
        slicni = topMatches(itemsBased,movie)
        for slicen in slicni:
            if slicen[1] in notWatch:
                if slicen[0] >= 0:
                    if slicen[1] in added:
                        if slicen[0] > added[slicen[1]]:
                            added[slicen[1]]=slicen[0]
                    else:
                        added[slicen[1]]=slicen[0]
    reverse = []
    for x in added:
        reverse.append((added[x],x))
    reverse.sort()
    reverse.reverse()
    if len(reverse) > 3:
        for x in range(0,3):
            rankings.append(reverse[x][1])
    else:
        for x in range(0,len(reverse)):
            rankings.append(reverse[x][1])
    rankings.sort()
    return rankings


if __name__ == "__main__":
    korisnik=input()
    print "user-based: %s"%(getUserBasedRecomendations(oceniPoKorisnici,korisnik))
    print "item-based: %s"%(getItemBasedRecomendations(oceniPoKorisnici,korisnik))