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2019.06.22 14:31

추천시스템

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# A dictionary of movie critics and their ratings of a small
# set of movies
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}}

# set of 레스토랑
"""
critics={
'김수영': {'이락 흑태찜': 2.5, '다솜차반 한정식': 3.5, '도덕봉가든 오리백숙': 3.0,
 '흑룡산 촌두부': 3.5, '더리스 바베큐': 2.5, '샤브웰 해물뷔페': 3.0,
 '대나무통밥 연잎정식': 5.0},
'박상미': {'이락 흑태찜': 3.0, '다솜차반 한정식': 3.5, '도덕봉가든 오리백숙': 1.5,
 '흑룡산 촌두부': 5.0, '샤브웰 해물뷔페': 3.0, '더리스 바베큐': 3.5, '수통골양촌장어': 2.5},
'장성욱': {'이락 흑태찜': 2.5, '다솜차반 한정식': 3.0, '도덕봉가든 오리백숙': 3.5,
 '흑룡산 촌두부': 3.5, '샤브웰 해물뷔페': 4.0, '대나무통밥 연잎정식': 3.5, '수통골양촌장어': 3.0},
'천성욱': {'다솜차반 한정식': 3.5, '도덕봉가든 오리백숙': 3.0, '샤브웰 해물뷔페': 4.5,
 '흑룡산 촌두부': 4.0, '더리스 바베큐': 2.5, '대나무통밥 연잎정식': 4.0, '수통골양촌장어': 3.0},
'백두산': {'이락 흑태찜': 3.0, '다솜차반 한정식': 4.0, '도덕봉가든 오리백숙': 2.0,
 '흑룡산 촌두부': 3.0, '샤브웰 해물뷔페': 3.0, '더리스 바베큐': 2.0, '수통골양촌장어': 5.0},
'한라산': {'이락 흑태찜': 3.0, '다솜차반 한정식': 4.0, '샤브웰 해물뷔페': 3.0,
 '흑룡산 촌두부': 5.0, '더리스 바베큐': 3.5, '대나무통밥 연잎정식': 5.0, '수통골양촌장어': 4.0},
'홍길동': {'다솜차반 한정식':4.5,'더리스 바베큐':1.0,'흑룡산 촌두부':4.0,
 '대나무통밥 연잎정식': 2.5}}
"""

# 영화평론가 평가표
"""
영화평론={'강감찬': {'택시운전사': 2.5, '남한산성': 3.5,
 '명량': 3.0, '국제시장': 3.5, '소나기': 2.5,
 '반지의제왕': 3.0},
'박문수': {'택시운전사': 3.0, '남한산성': 3.5,
 '명량': 1.5, '국제시장': 5.0, '반지의제왕': 3.0,
 '소나기': 3.5},
'곽재우': {'택시운전사': 2.5, '남한산성': 3.0,
 '국제시장': 3.5, '반지의제왕': 4.0},
'이성계': {'남한산성': 3.5, '명량': 3.0,
 '반지의제왕': 4.5, '국제시장': 4.0,
 '소나기': 2.5},
'이율곡': {'택시운전사': 3.0, '남한산성': 4.0,
 '명량': 2.0, '국제시장': 3.0, '반지의제왕': 3.0,
 '소나기': 2.0},
'김유신': {'택시운전사': 3.0, '남한산성': 4.0,
 '반지의제왕': 3.0, '국제시장': 5.0, '소나기': 3.5},
'홍길동': {'남한산성':4.5,'소나기':1.0,'국제시장':4.0}}
"""

from math import sqrt

# Returns a distance-based similarity score for person1 and person2
def sim_distance(prefs,person1,person2):
  # Get the list of shared_items
  si={}
  for item in prefs[person1]:
    if item in prefs[person2]: si[item]=1

  # if they have no ratings in common, return 0
  if len(si)==0: return 0

  # Add up the squares of all the differences
  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)

# Returns the Pearson correlation coefficient for p1 and p2
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

# Returns the best matches for person from the prefs dictionary.
# Number of results and similarity function are optional params.
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]

# Gets recommendations for a person by using a weighted average
# of every other user's rankings
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

def transformPrefs(prefs):
  result={}
  for person in prefs:
    for item in prefs[person]:
      result.setdefault(item,{})
     
      # Flip item and person
      result[item][person]=prefs[person][item]
  return result


def calculateSimilarItems(prefs,n=10):
  # Create a dictionary of items showing which other items they
  # are most similar to.
  result={}
  # Invert the preference matrix to be item-centric
  itemPrefs=transformPrefs(prefs)
  c=0
  for item in itemPrefs:
    # Status updates for large datasets
    c+=1
    if c%100==0: print ("%d / %d" % (c,len(itemPrefs)))
    # Find the most similar items to this one
    scores=topMatches(itemPrefs,item,n=n,similarity=sim_distance)
    result[item]=scores
  return result

def getRecommendedItems(prefs,itemMatch,user):
  userRatings=prefs[user]
  scores={}
  totalSim={}
  # Loop over items rated by this user
  for (item,rating) in userRatings.items( ):

    # Loop over items similar to this one
    for (similarity,item2) in itemMatch[item]:

      # Ignore if this user has already rated this item
      if item2 in userRatings: continue
      # Weighted sum of rating times similarity
      scores.setdefault(item2,0)
      scores[item2]+=similarity*rating
      # Sum of all the similarities
      totalSim.setdefault(item2,0)
      totalSim[item2]+=similarity

  # Divide each total score by total weighting to get an average
  rankings=[(score/totalSim[item],item) for item,score in scores.items( )]

  # Return the rankings from highest to lowest
  rankings.sort( )
  rankings.reverse( )
  return rankings

def loadMovieLens(path='C:/Python34'):
  # Get movie titles
  movies={}
  for line in open(path+'/uitem.txt'):
    (id,title)=line.split('|')[0:2]
    movies[id]=title
 
  # Load data
  prefs={}
  for line in open(path+'/udata.txt'):
    (user,movieid,rating,ts)=line.split('\t')
    prefs.setdefault(user,{})
    prefs[user][movies[movieid]]=float(rating)
  return prefs
 


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